# Quantum Technology for Military Applications

Michal Krelina<EMAIL_ADDRESS>Faculty of Nuclear Sciences and Physical
Engineering, Czech Technical University in Prague, Brehova 7, Prague, Czech
Republic  Quantum Phi s.r.o., Bryksova 944, Prague, Czech Republic

###### Abstract

Quantum technology is an emergent and potentially disruptive discipline, with
the ability to affect many human activities. Quantum technologies are dual-use
technologies, and as such are of interest to the defence and security industry
and military and governmental actors. This report reviews and maps the
possible quantum technology military applications, serving as an entry point
for international peace and security assessment, ethics research, military and
governmental policy, strategy and decision making. Quantum technologies for
military applications introduce new capabilities, improving effectiveness and
increasing precision, thus leading to ‘quantum warfare’, wherein new military
strategies, doctrines, policies and ethics should be established. This report
provides a basic overview of quantum technologies under development, also
estimating the expected time scale of delivery or the utilisation impact.
Particular military applications of quantum technology are described for
various warfare domains (e.g. land, air, space, electronic, cyber and
underwater warfare and ISTAR—intelligence, surveillance, target acquisition
and reconnaissance), and related issues and challenges are articulated.

Keywords: quantum warfare, quantum technology, quantum computing, quantum
sensing, quantum network, quantum radar, quantum imaging, military
applications, quantum security, dual-use technology

##  1 Introduction

Although fourth generation modern warfare is characterised by decentralisation
and the loss of states’ monopoly on war [1, 2], armies of advanced countries
characteristically have access to state-of-the-art military technologies. This
includes the appearance of quantum technologies on the horizon.

The term quantum technology (QT) means the technology mostly arising out of
the so-called second quantum revolution. Earlier, the first quantum revolution
brought technologies that are familiar to us today, such as nuclear power,
semiconductors, lasers, magnetic resonance imaging, modern communication
technologies or digital cameras and other imaging devices. The first quantum
technology resulted in nuclear weapons and energy; then, the classical
computer gained a significant role. Presently, laser weapons are being
implemented and tested [3].

The second quantum revolution [4] is characterised by manipulating and
controlling individual quantum systems (such as atoms, ions, electrons,
photons, molecules or various quasiparticles), allowing to reach the standard
quantum limit; that is, the limit to measurement accuracy at quantum scales.
In this report, the term quantum technology refers to the technology from the
second quantum revolution. Quantum technology does not bring fundamentally new
weapons or standalone military systems, but rather significantly enhances
measurement capability, sensing, precision and computation power and
efficiency of the current and future military technology. Most of the quantum
technologies typically are technologies of dual use. Consequently, there is
tremendous potential for military applications of quantum technology. Various
studies and recommendations are emerging, signalling the increasing likelihood
of such technology being realised; see, for example, [5, 6, 7, 8].

This report provides a more in-depth context in which to understand the term
‘quantum warfare’, discussing the possibility of its affecting the
intelligence, security and defence sectors, and describing new possible
capabilities or improvements. The goal is not to provide a precise forecast of
quantum-based technologies, but rather to show possible directions and trends
in implementation and applications. Quantum technologies in general are
considered emerging technologies, with the potential to change the conduct of
warfare and the outcomes of battles [8]. Although the current quantum
technologies mostly have low Technology Readiness Levels (TRL), they are
believed to have disruptive potential [9]. The mapping of quantum
technologies’ conceivable military applications is also important for the
further assessment of threats to global peace and in the discussion of ethics
policies or quantum-based preventive arms control.

This report comprises eight sections. In Section 2, the terms ‘quantum
technology’ and ‘quantum warfare’ are defined, with the quantum technology
taxonomy and quantum technologies being introduced. Section 3 provides the
basic quantum technology overview that is the foundation for a particular
application, including the expected time of deployment and utilisation impact.
Section 4 presents the general considerations and expectations regarding
quantum technology development and deployment in the military domain. In
Section 5, the applications of individual quantum technologies in the military
are presented for various domains ( e.g. cyber, underwater, space and
electronic warfare). Section 6 identifies and discusses the quantum hype as
well as the realistic possibilities. Section 7 contains the initial discussion
on related military, peace and ethical aspects as well as the technical
consequences and challenges. Section 8 concludes the paper.

Sections 5 and 4 concern national security and defence issues. Although
Section 3 is based on state-of-the-art research and provides related
references, Section 5 is based more on various military or government reports,
policy briefs and international security analyses such as [5, 6, 7, 8, 10, 11,
12, 13]. Here, the reader should be wary of the hype surrounding quantum
technology and avoid exaggerated expectations; this aspect is addressed in
Section 6 and by [14]. For many of the presented quantum technology military
applications, it is uncertain whether all challenges connected with the
demands of high-end military technologies will be resolved, or even that the
technology will actually be deployed.

##  2 Definitions

The term quantum technology is defined as follows:

> Quantum technology (QT) is an emerging field of physics and engineering
> based on quantum-mechanical properties—especially quantum entanglement,
> quantum superposition and quantum tunnelling—applied to individual quantum
> systems, and their utilisation for practical applications.

As follows from the definition, quantum technology describes the various
physical principles of quantum-mechanical systems, with numerous applications;
for instance, the technique of trapped ions can serve as a quantum bit for
quantum computers and as a quantum sensor for magnetic fields or quantum
clocks.

> Dual-use technology refers to fields of research and development with
> potential application in both defence and commercial production [15].

Quantum technology is a typical dual-use technology which has been of
considerable interest not only for military but also for governmental actors
[16] and peacekeeping organisations.

> Quantum warfare (QW) is warfare that uses quantum technologies for military
> applications that affect intelligence, security and defence capabilities of
> all warfare domains, and it ushers in new military strategies, doctrines,
> scenarios and peace as well as ethics issues.

There have been attempts also to define the quantum domain [17] as a new
domain for warfare. However, in this text, we will consider quantum technology
as a factor that improves all currently defined domains, rather than as a
standalone warfare domain.

Subsequently, it is helpful to define the term quantum attack, which refers to
using quantum technologies to break, disrupt or eavesdrop on either classical
or quantum security systems. Typical examples are eavesdropping using quantum
key distribution or quantum computers breaking the Rivest–Shamir–Adleman (RSA)
encryption scheme.

Although there is plenty of QT literature, there is no explicit agreement on
quantum technology taxonomy. We will use the following taxonomy:

  * •

Quantum Computing and Simulations

    * –

Quantum Computers (digital and analogue quantum computers and their
applications, such as quantum system simulation, quantum optimisation, …)

    * –

Quantum Simulators (non-programmable quantum circuits)

  * •

Quantum Communication and Cryptography

    * –

Quantum Network and Communication (quantum network elements, quantum key
distribution, quantum communication)

    * –

Post-Quantum Cryptography (quantum-resilient algorithms, quantum random number
generator)

  * •

Quantum Sensing and Metrology

    * –

Quantum Sensing (quantum magnetometers, gravimeters, …)

    * –

Quantum Timing (precise time measurement and distribution)

    * –

Quantum Imaging (quantum radar, low-SNR imaging, …)

Aside from the general quantum technology taxonomy presented above, we
introduce a new division of quantum technologies according to their benefits
and utilisation. The following classification can be generalised; however, we
place more emphasis on military applications. The quantum technology
utilisation impact classification is as follows:

  * •

Must have: quantum technology that has to be implemented to protect against
future quantum attacks (e.g. post-quantum cryptography);

  * •

Effectiveness: quantum technologies that increase the effectiveness of the
current technology and methods (e.g. quantum optimisations, quantum machine
learning or artificial intelligence);

  * •

Precision: quantum technologies that increase the precision of the current
measurement technology (e.g. quantum magnetometry, quantum gravimetry, quantum
inertial navigation, timing);

  * •

New capabilities: quantum technologies offering new capabilities that were
beyond the scope of the present technology (e.g. quantum radar, quantum
simulation for chemistry, quantum cryptoanalysis, quantum key distribution).

Note that this classification is not mutually exclusive.

##  3 Quantum Technology Overview

This section provides a basic description of quantum technologies, with
related references. For each quantum technology, the current development
status is presented, the utilisation impact determined, expected time to
deployment estimated111 Short-term: 0–5 years, Mid-term: 6–10 years, Long
term: 10–20 years. , and the main challenges are sketched. For quantum
computing application, the approximate number of required logical qubits is
provided.

Different quantum technologies and their applications are at different
TRLs222See Technology Readiness Levels according to EU,
https://ec.europa.eu/research/participants/data/ref/h2020/wp/2014_2015/annexes/h2020-wp1415-annex-
g-trl_en.pdf from TRL 1 (e.g., some types of qubits) to TRL 8 (e.g., quantum
key distribution).

We are not aiming for completeness here, nor do we present any theoretical
background, but just introduce the basics, the effects and the current state
of development, as needed to follow the discussed military applications.

###  3.1 Quantum Information Science

Quantum information science (QIS) is an information science related to quantum
physics, and deals with quantum information. In classical information science,
the elementary carrier of information is a bit that can be only 0 or 1. The
quantum information elementary carrier of information is the quantum bit,
qubit in short. A qubit can be $|0\rangle$ or $|1\rangle$, or an arbitrary
complex linear combination of states $|0\rangle$ and $|1\rangle$ called the
quantum superposition.

The other crucial property is the quantum entanglement. Quantum entanglement
refers to a strong correlation between two or more qubits (or two or more
quantum systems in general) with no classical analogue. Quantum entanglement
is responsible for many quantum surprises. Another feature is the no-cloning
theorem [18], which says that quantum information (qubit) cannot be copied.
This theorem has profound consequences for qubit error correction as well as
for quantum communication security.

Quantum information science describes the quantum information flow in quantum
computing and quantum communications, although in a broader sense it can be
applied in quantum sensing and metrology, see [19, 20].

There is considerable academic interest, and several quantum algorithms have
been created [21]. However, only a few are expected to be valuable for defence
and security applications.

###  3.2 Quantum Computing

  * •

Status:  commercially available with very limited number of physical qubits

  * •

Utilisation impact:  new capability, effectiveness, precision

  * •

Timeline expectation:  one million physical qubits in ten years

  * •

Main challenges:  improving the quality of qubits (coherence, error
resistance, gate fidelity), upscaling the number of qubits, logical qubits

Quantum computing refers to the utilisation of quantum information science to
perform computations. Such a machine can be called a quantum computer. The
classification of quantum computers can be very complex. For the purposes of
this report, we simplify the classification as follows:

  * •

Digital quantum computer (also called a gate-level quantum computer) is
universal, programmable and should perform all possible quantum algorithms and
have numerous applications described below. Classical computers can fully
simulate the gate-level based quantum computer. The difference is in resources
and speed. For instance, the simulation of fully entangled qubits increases
the requirement of classical resources exponentially. This means that the
simulation of $\gtrsim 45$ qubits is practically impossible on the classical
(super)computers.

  * •

Analogue quantum computer (also called Hamiltonian computation) is usually
realised using quantum annealing (as the noise version of the adiabatic
quantum computing). Quantum annealer differs from the digital quantum computer
by the limited connectivity of qubits and different principles. Therefore, the
utilisation of analogue quantum computers is more constrained but is still
suitable for tasks such as quantum optimisations or Hamiltonian-based
simulations.

  * •

Quantum simulator is used for the study and simulation of other quantum
systems that generally are less accessible and is usually built as a single-
purpose machine. In comparison with a quantum computer, the quantum simulator
can be imagined as a non-programmable quantum circuit.

In general, quantum computing will not replace classical computation. Quantum
computers will be practical and useful for a limited type of problems only,
typically problems with high complexity. The actual deployment of quantum
computing applications depends on the quality (coherence, error resistance,
gate fidelity) and the number of qubits. Some of the essential parameters to
follow are: the number of qubits, qubit coherence time, quantum-gate fidelity
and qubit inter-connectivity. The set of quantum instructions applying quantum
gates on individual qubits is called a quantum circuit. A quantum circuit is a
practical realisation of the quantum algorithm.

Following [7], quantum computers can be classified into three evolutionary
stages: Component quantum computation (CQC), Noisy intermediate-scale quantum
(NISQ) computing and Fault-tolerant quantum computing (FTQC). The CQC stage
covers quantum computing demonstrators and maturing the basic elements. CQC
has a very limited computational capability that is sufficient for
demonstration of some proofs of principle. The NISQ stage quantum computer
should have a sufficient number of qubits to demonstrate the advantages of
quantum computing. Continuous research should lead to increasing the number
and quality of qubits. The FTQC stage starts when a perfect logical qubit is
reached (for an explanation, see below).

Physical qubits can be realised by numerous quantum systems. The most recent
advanced are quantum computer based on superconducting qubits and the trapped-
ion qubits that are in or close to the NISQ stage. All other technologies,
such as cold atoms, topological, electron spin, photonic or NV centre-based
qubits, are still in CQC stage or theory only. The individual quantum
computers and their performance differ significantly (in, e.g. speed,
coherence time, the possibility to entangle all qubits, gate fidelity).
Various metrics and benchmarks, such as Quantum Volume metric [22], have been
developed for their comparison.

The problem, common to all types of qubits, is their quality. A qubit is very
fragile and has a limited coherence time (a time scale during which it will
not lose the quantum information). Every operation performed on a qubit has
limited fidelity. Researchers accordingly need to employ the error correction
codes. The error correction for qubits is much more complicated than error
correction of classical bits, because qubits cannot be copied, as the no-
cloning theorem explains. Two types of qubits are distinguished: the physical
qubit realised by a physical quantum system and the logical qubit consisting
of several physical qubits and error correction codes. A logical qubit is a
perfect or near-perfect qubit with very long-to-infinity coherence time, very
high fidelity and higher environment resistivity. For example, based on
surface error correction protocol, for one logical qubit, depending on the
algorithm, up to 10 000 physical qubits [23] will be needed. For a recent
overview of quantum computing, see, for example, [24].

Examples of leading-edge quantum computers are the quantum computer with 53
physical superconducting qubits, manufactured by Google (which claimed quantum
supremacy in 2019 [25]), and the one by IBM. The best trapped-ion quantum
computers are of 32 qubits by IonQ or six qubits by Honeywell. In the case of
photonic qubits, there is a 24-qubit quantum computer by Xanadu. The
anticipated timelines as imagined by the quantum computing roadmap by IBM and
Google are the following: IBM plans a 433-qubit quantum processor in 2022 and
1,121 qubits by 2023 [26]. Google has announced a plan to reach a quantum
module of 10,000 qubits. All other quantum processors would consist of such
modules up to 1 million qubits in 2029 [27]. Based on a survey among leaders
in key relevant areas of quantum science and technology, it is likely that
quantum computers will start to become powerful enough to pose a threat to
most of the public key encryption schemes (for more details, see Sec. 3.2.2)
in about two decades [28]. Examples of analogue quantum computers are the
quantum annealer by D-Wave Systems with over 5,000 qubits and the coherent
Ising machine by Toshiba.

The difference between analogue and digital quantum computers lies in their
different physical principles and their limitations. The digital quantum
computer is limited by resources and not by noise (noise can be corrected
using more resources). In contrast, the analogue quantum computer is limited
by noise which is difficult to understand, control and characterise
(especially for a quantum annealer). Therefore, analogue quantum computers’
applicability is limited [24].

In reality, the tasks accomplished by quantum computers will be mostly only
subprograms or subroutines of the classical computer programs. The classical
program will not only control quantum computers but will also provide a lot of
computation that it would be impractical to carry out on a quantum computer.
This includes the recent applications of quantum simulation in chemistry
using, for example, the Variational quantum eigensolver (VQE) [29], which is a
hybrid combination of classical and quantum computing. Also, quantum computers
are large machines, many of which require cryogenics. It is unlikely therefore
that in the decades to come most customers will acquire a personal quantum
computer, but rather they will access these as a service in the cloud. The
cloud-based models of quantum computing (often called Quantum Computing-as-a-
Service – QCaaS) are commercially available nowadays, even for free, and they
allow access to anyone interested in quantum computing. The cloud access to
quantum computers is offered by individual quantum hardware manufacturers.
Some platforms, such as Microsoft Azure Quantum or Amazon Braket, offer access
to quantum computers of various manufacturers within one ecosystem.

It is also helpful to clarify the terms of quantum supremacy, advantage and
practicality. Quantum supremacy is a case where a particular problem is solved
by a quantum computer significantly faster than by a classical computer.
However, the problem is likely to be theoretical rather than practical.
Quantum advantage refers to a case when a quantum computer is able to solve
real-world problems that classical computers cannot. Quantum practicality is
similar to quantum advantage, with the only difference being that the quantum
computer solves real-world problems faster than the classical computer.

We provide below a basic overview of possible quantum computer applications.
The reader should keep in mind that quantum computing is a fast developing
sector, and new revolutionary quantum algorithms are still waiting to be
discovered. Note that, in the context of quantum computing applications, the
term ‘qubit’ implies a logical qubit. However, small quantum circuits can be
run with only physical qubits, with reasonable precision.

####  3.2.1 Quantum Simulations

  * •

Status:  algorithms in development, small-scale applications

  * •

Utilisation impact:  new capability (e.g. quantum chemistry computation)

  * •

Timeline expectation: short-term, usability scales up with the number of
qubits

  * •

Qubits requirement:  $\sim 200$ (e.g. for nitrogen fixation problem)

  * •

Main challenges:  number of logical qubits

Long before the first quantum computer was created, the main task for the
quantum computer was considered the simulation of other quantum systems [30].
Molecules are such a quantum system. Despite advancement of extant computing
power, the full simulation of only simpler molecules can be performed using
present computational chemistry, or of larger molecules for the price of many
approximations and simplifications. For example, for a system with $n$
electrons, the classical computer would need $2^{n}$ bits to describe the
state of electrons, whereas the quantum computer needs only $n$ qubits.
Therefore, quantum simulations are the first and still perhaps the most
promising application for quantum computers.

The most dominant approaches are two: quantum-phase estimation [31] and
quantum variational techniques (VQE) [32, 33]. The latter approach in
particular has the highest likelihood of success on NISQ computers; for
example, in 2020, Google performed the biggest quantum chemical simulation up
to date (of H12 molecule using the VQE) [34].

Algorithms for quantum chemistry simulations are being developed. They can be
applied to more complex simulations, hand in hand with the number of qubits.
Therefore, even at this early stage of quantum computing, there is significant
interest from the chemical and pharmaceutical industries. In general, such
simulations allow the discovery and design of new drugs, chemicals and
materials. Recently considered topics, for instance, are high-temperature
superconducting, better batteries, protein folding, nitrogen fixation and
peptides research.

####  3.2.2 Quantum Cryptoanalysis

  * •

Status:  algorithms ready

  * •

Utilisation impact:  new capability (e.g. public-key cryptography schemes
breaking)

  * •

Timeline expectation: mid- to long-term

  * •

Qubits requirement:  $\sim 6,200$ for 2048 bit RSA factorisation [35], $\sim
2,900$ for 256 bit ECDLP-based333 Cryptography based on Elliptic Curve
Discrete Logarithm Problem.  encryption [36]

  * •

Main challenges:  number of logical qubits

One of the most well-known quantum computer applications is the factorisation
of large prime numbers by exponential speedup described by Shor’s algorithm
[37]. This is a threat for public-key cryptography schemes, such as RSA, DH
and ECC444cryptography schemes named after Rivest–Shamir–Adleman,
Diffie–Hellman, Elliptic Curve Cryptography, based on the large prime number
multiplications, the discrete logarithm problem or the elliptic-curve discrete
logarithm problem-based schema that are considered computationally intractable
or very hard for classical computers.

Although the resources of existing NISQ quantum computers are far from what is
needed for RSA breaking, the threat is quite real. An adversary or foreign
intelligence could intercept and store encrypted traffic until the quantum
cryptoanalysis becomes available. Because the time of declassification of many
secrets is far beyond the expected timelines for powerful quantum computer
delivery, such a threat can be considered genuine, nowadays.

Quantum cryptoanalysis also offers improved tools for a brute-force attack on
the symmetric encryption schemes. For example, the well-known Grover’s
searching algorithm [38] reduces the key security by half against a brute-
force attack; a 256-bit AES555Advanced Encryption Standard key could be
resolved by brute force in roughly $2^{128}$ quantum operations. Despite the
huge resource requirements of quantum computers, doubling the symmetric key
length [39]is recommended, nevertheless. Moreover, Simon’s algorithm and
superposition queries [40] can completely break most message authentication
code (MAC), and authenticated encryption with associated data (AEAD), such as
HMAC-CBC and AES-GCM666Hash-based Message Authentication Code-Cipher Block
Bhaining, AES with Galois/Counter Mode [41, 42].

Further, there is active research on cryptoanalytic attacks upon symmetric key
systems based on the structure present in symmetric cryptosystems, which can
offer up to super-polynomial speedup [43]. However, these algorithms suffer
from excessive resource requirements on the quantum computer.

####  3.2.3 Quantum Searching and Quantum Walks

  * •

Status:  algorithms under development

  * •

Utilisation impact:  effectiveness (e.g. faster searching)

  * •

Timeline expectation: short- to mid-term

  * •

Qubits requirement:  $\sim 100$, depends on the searched system size

  * •

Main challenges:  number of logical qubits

One of the most famous searching quantum algorithms is the Grover’s algorithm
[38], which offers quadratic speedup in database searching, or generally in
inverting a function. For an unsorted list or database, the classical
searching algorithms are about complexity $\mathcal{O}(N)$ (meaning
proportional to the number of $N$ entities), although Grover’s algorithm is
about $\mathcal{O}(\sqrt{N})$.

Quantum searching algorithms are an important topic for the so-called Big Data
(unstructured data) analysis. Working on a large amount of data requires a
large quantum memory. However, there is no reliable quantum memory that would
keep the quantum information for an arbitrarily long time and in large
amounts. Second, the transformation of classical data to the quantum form is
time-consuming and ineffective. Therefore, only the searching on data
generated algorithmically is considered practical at the moment.

The other approach to searching can be based on the quantum random walk
mechanism [44], which offers similar speedup as Grover’s algorithm.

####  3.2.4 Quantum Optimisations

  * •

Status:  algorithms in development

  * •

Utilisation impact:  effectiveness (e.g. faster solution of NP problems)

  * •

Timeline expectation: short- to mid-term

  * •

Qubits requirement:  $\sim$ 100, depends on the problem complexity

  * •

Main challenges:  number of logical qubits

Quantum optimisation is a very actively explored topic, given the possibility
of solving NP-level777NP is a complexity class characterised by the fact that
it cannot be solved in polynomial time but can be verified in polynomial time.
Specifically, the NP-hard problems are not only hard to solve but are
difficult to verify as well. Examples of NP-hard problems are the Travelling
Salesman Problem and Graph Colouring problems. complex problems. An example of
such an NP problem is the travelling salesman problem. Here, given a list of
places and the distances between them, the goal is to find the shortest (and
optimal) route. Naively, one can try all possibilities, but such an approach
has severe disadvantages, and may even become impossible, with increasing
complexity. Therefore, the most common solutions are based on heuristic
algorithms that are not necessarily guaranteed to find the best solution but
at least one close to it.

Quantum computing introduces a new perspective on the issue and offers
different approaches and techniques. The most dominant methods are currently
based on a variational approach, such as the Quantum approximate optimisation
algorithm (QAOA) [45]. Part of QAOA is the sub-technique called Quadratic
unconstrained binary optimisation (QUBO) [46], which is also suitable for
analogue quantum computers. Other methods are, for example, the quantum
analogy of the least-squares fit [47] or semidefinite programming [48].

So far, it is not clear whether the quantum optimisation will offer some
speedup against the classical heuristic methods. However, there is consensus
that if at all some speedup is achievable, it will not be more than polynomial
[48]. A new paradigm introduced by quantum computing leads to new quantum-
inspired classical algorithms, such as in the case of QAOA [49] that delete
the quantum speedup. On the other hand, we can speak about quantum-inspired
algorithms as of the first quantum computing practical result.

There have been many demonstrations, use cases and proofs of concept for
quantum optimisations, especially in connection with analogue quantum
computing that currently offers the most quantum computing resources for such
applications. The typical demonstrations were optimisations for traffic,
logistics or the financial sector888For examples of developed quantum
optimisation applications at D-Wave’s quantum annealer, see
https://www.dwavesys.com/applications..

####  3.2.5 Quantum Linear Algebra

  * •

Status:  algorithms in development

  * •

Utilisation:  effectiveness (e.g. faster linear equation solving)

  * •

Timeline expectation: short- to mid-term

  * •

Qubits requirement:  depends on the solved system size

  * •

Main challenges:  number of logical qubits

It has been shown that quantum computers can reach super-polynomial speedup
for solving linear equations also. Such a speedup was estimated especially for
the HHL (Harrow-Hassidim-Lloyd) [50] algorithm for sparse matrices. However,
the estimated speedup depends on the size of the problem (matrix). There are
also large resource requirements, which for some problems can be considered
too impractical [51]. On the other hand, for the system of linear equations of
10,000 parameters, for instance, 10,000 steps are needed to solve it, whereas
the HHL can provide an approximate solution after 13 steps.

At present, many numerical simulations in planning, engineering, construction
and weather forecasting simplify complex problems as a large set of linear
equations. For many of them, being statistical in nature, the approximated
solution could be sufficient.

Note that the HHL algorithm was shown as universal for quantum computing and
was demonstrated for various applications such as $k$-mean clustering, support
vector machines, data fitting, etc. For more details, see [52].

One of the major caveats of quantum algorithms working with a large amount of
input data is data loading. Classical data, especially binary data or bits,
need to be transferred into quantum states for follow-on processing by
efficient quantum algorithms. This process is slow, and the classical data
loading itself can take longer than the coherence time. The solution is a
quantum memory or quantum RAM [52, 53].

####  3.2.6 Quantum Machine Learning and AI

  * •

Status:  algorithms in development

  * •

Utilisation impact:  effectiveness (e.g. better machine learning optimisations
)

  * •

Timeline expectation: short- to mid-term

  * •

Qubits requirement:  $\sim 100$, depends on the problem complexity

  * •

Main challenges:  number of logical qubits

Due to the hype around classical machine learning and artificial intelligence
(ML/AI), it can be expected that there will be quantum research on this topic
also. First, note that one cannot expect full quantum ML/AI, considering the
very low efficiency of working with classical data [54], all the more so if
the missing quantum memory and very slow loading and coding of classical data
(e.g. data of picture) into quantum information format are considered. It is
simply not practical. Another situation will emerge when ML/AI is applied to
quantum data; for instance, from quantum sensors or imaging [55].

Nevertheless, quantum-enhanced ML/AI [56, 57] can be introduced, where quantum
computing can improve some machine learning tasks such as quantum sampling,
linear algebra (where machine learning is about the processing of complex
vectors in a high-dimensional linear space) or quantum neural networks [54].
One example is the quantum support vector machine [58].

In fact, the ML/AI topic covers various techniques and approaches, and it is
no different in connection with quantum computing. Quantum ML/AI or quantum-
enhanced ML/AI is the subject of many research works nowadays. For a survey of
quantum ML/AI algorithms and their possible speedup, see [59].

###  3.3 Quantum Communication and Cryptography

Quantum communication refers to a quantum information exchange via a quantum
network that uses optical fibre or free-space channels. In most cases, quantum
communication is realised using a photon as the quantum information carrier.
However, due to the limitations of photons, such as losses at large distances,
the quantum network contains other elements such as a quantum repeater or
quantum switch.

The goal of quantum cryptography is to replace conventional (mainly
asymmetric) encryption schemes with quantum-resistant algorithms with the
quantum key distribution. The typical quantum features used for quantum
communication are the following: quantum entanglement, quantum uncertainty,
and the no-cloning theory which states that quantum information cannot be
copied [18, 60].

####  3.3.1 Quantum Network

  * •

Status:  in research (commercially available for QKD with trusted nodes only)

  * •

Utilisation impact:  new capability, effectiveness (e.g. ultra secure
communication, quantum-resilient cryptography)

  * •

Timeline expectation: mid-term

  * •

Main challenges:  quantum repeater and switch (quantum memory)

The aim of the quantum network (sometimes called quantum internet [61] or
quantum information network (QIN)) is to transmit quantum information via
numerous technologies across various channels. The quantum information (qubit)
is usually carried by individual photons, and as such the quantum information
transmission is fragile. Moreover, many quantum network applications rely on
quantum entanglement.

The usual channels for quantum information transmission are specialised low-
loss optic fibres or the current telecommunication optic fibre infrastructures
with higher loss. The case of two communicating endpoints close to each other
is as simple as using one optical fibre. The complexity of the network
increases with more end nodes or large distances, where components such as a
quantum repeater or quantum switch are required. Note that very modest (one
qubit) quantum processors are sufficient for most quantum network
applications.

The free-space quantum channel is more challenging. Optical or near-optical
photons are of limited utility in the atmosphere due to the strong atmospheric
attenuation. Therefore, the most commonly considered and realised quantum
network scenario is using quantum satellites [62, 63]. The advantage of
satellites is the possibility of utilising optical-photon communication for
transmission of the quantum information, where the losses in the
satellite–ground link are lower than the loss between two ground nodes far
apart. Nevertheless, the optical photons’ communication in the free-space
channel for short distances can be realised using, for instance, drones [64].
The best way would be to use the microwave spectrum as employed by classical
wireless communication. However, communication that uses the microwave
spectrum at the level of individual photons is even more challenging [65].
Microwave single-photon technology involves greater difficulty in generating
and detecting individual photons. Another problem is a noisy environment in
microwave bands.

Quantum communication at long distances requires quantum repeaters due to
photon loss and decoherence. A quantum repeater is an intermediate node that
works similarly to the amplifier in classical optical networks but needs to
obey the no-cloning theorem. In fact, the quantum repeater allows entangling
qubits of end nodes. When two end nodes are entangled, the effect of quantum
teleportation [66] can be exploited. This means that the quantum information
can be teleported without a physically sent photon; just a classical
communication is required. Utilising quantum entanglement, the quantum
information can then flow through a quantum network or part of it, which can
even be under eavesdropper control without any chance of revealing the
transmitted quantum information. For correct functioning of the quantum
repeater, quantum memory is required. However, no reliable and practical
quantum memory is available yet.

As an intermediate step, a trusted repeater can be used. The trusted repeater
will not entangle end nodes and is used for the quantum key distribution (QKD,
see the next section, 3.3.2) only. To imagine how it works, let us consider
two parties $A$ and $B$ and a trusted repeater $R$. Then the key $k_{AB}$ is
encrypted with key $k_{AR}$. The trusted repeater $R$ decrypts $k_{AR}$ to get
$k_{AB}$. At this point, the trusted repeater $R$ knows the key $k_{AB}$, and
$A$ and $B$ have to trust that the key is safe and not under the control of
the eavesdropper. Finally, $R$ re-encrypts $k_{AB}$ using the $k_{RB}$ key and
sends it to $B$. This is a technique used in present QKD networks.

The next step, currently tested in experiments, is the measurement device-
independent QKD (MDI-QKD) [67, 68]. It is a quantum protocol that not only
replaces trusted repeaters (still not quantum, no support of entanglement)
with secure repeaters, but also serves as a switch. That means the usual star
network topology and infrastructure can start to be built. Note that in the
MDI-QKD network, attacks on the central node physically cannot reveal the key
nor reveal sensitive information. Later, the central nodes will be replaced by
the quantum switch and repeater, and the fully functional quantum information
network will be achieved.

Quantum networks will work in parallel with the classical networks, since not
all transmitted information needs to be encoded in quantum information. In
fact, parallel classical network is required, for instance, for quantum
teleportation. Quantum networks can be used for the following applications:

  * -

Quantum key distribution (QKD), a secure transmission of cryptographic key
(see Section 3.3.2);

  * -

Quantum information transmission between quantum computers or quantum
computing clusters at large distances or for sharing of remote quantum
capabilities;

  * -

Blind quantum computing [69, 70] allowing to transmit a quantum algorithm to
quantum computer, perform computations and retrieve results without the owner
or eavesdropper knowing what the algorithm or result was;

  * -

Network clock synchronisation [71], see Section 3.4.2;

  * -

Secure identification [72] allowing identification without revealing
authentication credentials;

  * -

Quantum position verification [73] allowing to verify the position of the
other party;

  * -

Distributed quantum computing [74, 75] for several quantum computers, allowing
to compute tasks as one quantum computer;

  * -

Consensus and agreement tasks refering to the so-called Byzantine Agreement
(problem of decision of group on one output despite the intervention of an
adversary). The quantum version [76] can reach agreement in $\mathcal{O}(1)$
complexity in comparison with classical complexity $\mathcal{O}\sqrt{n/\log
n}$.

  * -

Entangled sensor network [77, 78] allowing improvement in the sensitivity of
the sensors and reduction of errors, and evaluating global properties rather
than gathering data about specific parts of a system.

A quantum network allows direct secure quantum communication between quantum
computers, where quantum data can be directly exchanged. This can be useful
for effective redistribution of computing tasks according to individual
quantum computer performance, mainly when an enormous task can be divided into
smaller tasks. Another case is the quantum cloud, where quantum data can be
shared between several quantum computers. Moreover, it is questionable whether
it will be possible to build one standalone high-performance quantum computer.
The realisation will be more likely via distributed quantum computing [74,
75], where many quantum computers will be connected via the quantum network.

####  3.3.2 Quantum Key Distribution

  * •

Status:  commercially available (with trusted repeaters)

  * •

Utilisation:  new capability

  * •

Timeline expectation: short-term

  * •

Main challenges:  secure quantum repeater (quantum memory), security
certification of the physical hardware

Quantum key distribution (QKD) is the most mature application of quantum
communication. The goal is to distribute a secret key between two or more
parties for encrypted data distributed via classical channels. Due to the no-
cloning theorem, any eavesdropper has to perform a measurement that is
detectable by communicating parties.

The dominant classes of protocols are two: one based on BB84 (Bennett-Brassard
1984) protocol [79] and the other the E91 (Ekert 1991) protocol [80]. The
dominant BB84 protocol is technically simpler but requires a quantum random
number generation (see Section 3.3.4), and the providing party has to prepare
a key before the distribution. Protocol E91 utilises quantum entanglement that
generates the key during the process of distribution, and all parties know the
key simultaneously. In this protocol, the quantum random number generator is
not required. However, the technical solution with quantum entanglement is
more challenging. Both classes of protocols are information-theoretically
secure.

Theoretically, the QKD is impenetrable during the transmission. However, the
typical vector of attack can focus on the final (receiver/transmitter) or
intermediate nodes where the hardware of the software layer can contain
vulnerabilities such as bugs in control software, an imperfect single-photon
source, parties verification problem, etc. This is an area of very active
research. For example, the imperfect physical hardware can be abused by the
so-called photon-number-splitting [81], or Trojan-horse [82] attacks. Here,
security certification of the hardware and software is necessary and will take
time.

Apart from trusted repeaters, the other weak point is the qubit transfer rate,
which is too slow to distribute long keys. A new high transfer rate of single-
photon sources can resolve the issue.

At present, QKD technology is commercially available as a point-point
connection at short distances or by using trusted repeaters at large
distances. The trusted repeater can be a space satellite, as was demonstrated
by China [62, 63].

####  3.3.3 Post-Quantum Cryptography

  * •

Status:  algorithms ready

  * •

Utilisation impact:  must have

  * •

Timeline expectation: short-term

  * •

Main challenges:  standardisation, implementation

Post-quantum cryptography (sometimes referred to as quantum-proof, quantum-
safe or quantum-resistant cryptography) represents an area of encryption
techniques that should resist future quantum computer attacks. Presently, this
is not true for most of the asymmetric encryption that uses public-key
technology. On the other hand, most of the symmetric cryptographic algorithms
and hash functions are considered relatively secure against attacks by quantum
computers [83]. Nevertheless, doubling the symmetric key length is recommended
[39].

Now, several approaches are considered as quantum-resistant. For example,
lattice-based cryptography [84], supersingular elliptic curve isogeny
cryptography [85], hash-based [86] cryptography , multivariate-based [87]
cryptography, code-based cryptography [88] and symmetric key quantum
resistance.

Unlike QKD, all these algorithms are not provably secure from a mathematical
perspective. Therefore, within the process of standardisation, all these
algorithms are rigorously tested and analysed, including the implementation.
There is no worst case where a quantum-resistant algorithm with bugs in
implementation could be cracked by a classical computer [89]. The most
followed standardisation process is the one by the U.S. National Institute of
Standards and Technology (NIST). The standardisation process is in the third
round [90], with three finalists (algorithms based on the lattice, code-based
and multivariate) and several alternate candidates. The NIST standardisation
process is expected to conclude in 2023-24. Regardless, more and more
commercial vendors are offering new quantum-resistant encryption solutions
now.

####  3.3.4 Quantum Random Number Generator

  * •

Status:  commercially available

  * •

Utilisation impact:  new capability (truly random number generation)

  * •

Timeline expectation: short-term

  * •

Main challenges:  increasing bit rate

Random number generators (RNG) are essential for many applications such as
Monte Carlo simulations and integration, crypto operations, statistics and
computer games. Nevertheless, the RNG in a classical computer, since it acts
deterministically, is not truly random, and is called pseudo-random number
generation. However, for many applications, the pseudo-RNG is sufficient.

On the other hand, generating strong keys is the cornerstone of security,
which can be achieved only by truly random RNG. One solution is a quantum
random number generator (QRNG) that is hardware-based. Moreover, QRNG is a
crucial part of BB84-based QKD protocols, to be provably secure.

QRNG can be used for any cryptography and makes all cryptography better. One
of the advantages of QRNG is that it can be verified and certificated [91],
unlike any other RNG.

###  3.4 Quantum Sensing and Metrology

Quantum sensing and metrology is the most mature quantum technology area,
which improves timing, sensing or imaging. For example, atomic clocks from the
first quantum revolution have been part of the Global Positioning System (GPS)
for almost half a century. The current quantum clocks are coming up with much
higher time measurement precision.

Quantum sensing stands for all quantum technologies that measure various
physical variables such as external magnetic or electric fields, gravity
gradient, acceleration and rotation. Quantum sensors can produce very precise
information about an electric signal, magnetic anomalies and for inertial
navigation.

Quantum imaging is a subfield of quantum optics exploiting photon
correlations, allowing suppression of noise and increasing the resolution of
the imagined object. Quantum imaging protocols are considered for quantum
radar, detecting objects in the optically impermeable environment, and in
medical imaging.

Quantum sensing and metrology technology relies on one or more of the
following features: quantum energy levels, quantum coherence and quantum
entanglement [92]. Individual quantum sensors have various metrics that vary
with the application. The common metrics are: sensitivity (a signal that gives
unity signal-to-noise ratio after 1 second of integration time), dynamic range
(minimal and maximal detectable signal), sampling rate (how often the signal
is sampled), operating temperature, etc. Derived key metrics include, for
example, spatial resolution at a certain distance and the time required to
achieve a specified sensitivity. The typical measure quantities are magnetic
and electric fields, rotation, times, force, temperature and photon counting.

####  3.4.1 Quantum Electric, Magnetic and Inertial Forces Sensing

  * •

Status:  laboratory prototypes

  * •

Utilisation impact:  precision, new capability

  * •

Timeline expectation: short- to long-term

  * •

Main challenges:  miniaturisation, cooling

Many sensing quantum technologies are universal and can measure various
physical quantities. A detailed description of each technology is outside the
scope of this report; however, a basic overview is provided. Many applications
include various quantum technologies. For example, quantum inertial navigation
consists of three types of sensing: acceleration, rotation and time. In
general, precise quantum-based timing is required for many applications, not
only for quantum technologies. For quantum timing, see Section 3.4.2. The most
promising technologies are: atomic vapour, cold-atom interferometry, nitrogen-
vacancy centres, superconducting circuits and trapped ions.

Cold-atom interferometry (measured quantities: magnetic field, inertial
forces, time). Atoms cooled at very low temperatures exhibit wave-like
behaviour and are sensitive to all forces that interact with their mass. The
changes are observed in the interference pattern [92, 93, 5]. The particular
realisation can be in the form of Raman atom interferometry, atom Bloch
oscillation or others [94, 95, 96]. For example, in gravimetry, the quantum-
based gravimeter has the potential to reach about several orders of magnitude
higher precision [5] than the best classical counterparts. Such a precise
gravimeter allows very detailed mapping of the Earth’s surface and underground
with a resolution at the centimetre level. Regarding inertial navigation, the
shaken lattice interferometry has the potential to overcome shortcomings of
the state-of-the-art atom interferometry techniques and can work as
accelerometer and gyroscope at once [97]. Several challenges remain. Some of
the biggest challenges are the integration of the quantum sensor into one
quantum inertial measurement unit, miniaturisation of laser cooling apparatus
used for cooling down atoms and simultaneously maintaining the coherency
(suppression of the interaction with the noisy environment), or the dynamic
range of the cold atom sensor outside the laboratory. However, significant
advances also can be found in this area, e.g. [98]. For a review see [99].

Trapped ions (measured quantities: electric and magnetic field, inertial
forces, time). Trapped ions are one of the most universal sensing platforms
[100, 101, 102]. Well-controlled trapped ions form a crystal with quantised
modes of motion. Any disturbance can be measured through the transition
between these modes. A single trapped ion can serve as a precise measurement
of time or as a qubit in a quantum computer. For inertial navigation, the
optical lattice technology of trapped cold atoms in 1, 2 and 3-dimensional
arrays potentially offers a sub-cm level in size. Besides allowing measurement
of gravitational and inertial parameters, it can measure Casimir or van der
Waals forces. More recently, using quantum-entangled trapped ions, measurement
of electric fields has reached a sensitivity of $\sim 240$ nV/m$s^{-1}$ [103],
which is several orders of magnitude better than the classical counterpart.

Nitrogen-vacancy (NV) centres (measured quantities: electric and magnetic
field, rotation, temperature, pressure). Nitrogen-vacancy centre in a diamond
crystal works as an electron spin qubit that couples with external magnetic
fields. In addition, negatively charged NV centres using Berry’s phase can
measure rotation. In general, NV centre-based sensors offer high sensitivity,
cheap production and operation in a wide range of conditions [92, 104, 105].
In particular, NV centre-based technology can also work at room temperature
and higher. A novel proposed 3D design allows to sense all three components of
magnetism, acceleration, velocity, rotation or gravitation simultaneously
[106]. The strengths of NV centres in diamond-based sensing are spatial
resolution and sensitivity. On the other hand, the challenge is choosing,
implementing and manufacturing individual NV centres or their ensembles. In
the case of electric field sensing, it is challenging to define a sensitivity
[107].

Superconducting circuits (measured quantities: electric and magnetic field).
The technology of superconducting circuits based on the Josephson effect
describes the quantum tunnelling effect between two superconductors [92]. This
technology allows manufacturing a quantum system at the macroscopic scale and
can be controlled effectively with microwave signals. The superconducting
quantum interference device (SQUID) is one of the best magnetometric sensors.
However, the disadvantage is the requirement of cryogenics. Note that for the
measurement of magnetic-field variations smaller than the geomagnetic noise,
the preferred design is based on an array of sensors to cancel the spatial-
correlation with applications, such as in medical and biomedical applications
(e.g. MRI or molecule tagging). The recent development shows that the
superconducting qubits used in quantum computers can be used to measure
electric and magnetic fields [92] as well.

Atomic vapour (measured quantities: magnetic field, rotation, time). Spin-
polarised high-density atomic vapour undergoes state transition under external
magnetic field which can be measured optically [92, 108, 109]. An advantage is
a deployment at room temperature. The atomic vapour is suitable for rotation
sensing, known as the Atomic Spin Gyroscopes (AGS). AGS can be chip-scale [5].
For comparison, the best classical rotation sensors are very precise (e.g.
ring laser gyroscope). The expected quantum sensor will be about twice as
precise. However, the mentioned best classical gyroscope has a size of
$4\times 4$ m, which is impractical [110]. Atomic vapour cell magnetometers
based on atomic ensembles have the potential to outperform SQUID magnetometers
and work at room temperature [92].

####  3.4.2 Quantum Clocks

  * •

Status:  laboratory prototypes

  * •

Utilisation impact:  precision

  * •

Timeline expectation: short- to mid-term

  * •

Main challenges:  miniaturisation

Atomic clocks have been with us for several decades; for example, as part of
GPS satellites. The current atomic clocks are based on atomic physics, where
the electromagnetic emissions from electrons when changing energy level
utilise a ‘tick’. As such, the atomic clock is a very mature technology.
Today, the atomic clocks based on atomic fountain or thermal atomic beam and
magnetic state selection principles can reach a relative uncertainty $\sim
10^{-15}-10^{-16}$ [111], or state-of-the-art chip-size atomic clocks have
uncertainty $2\times 10^{-12}$ [5].

The second quantum revolution comes with new principles for atomic or quantum
clocks. Quantum logic clock is based on single-ion, a technology related to
trapped-ion qubit for quantum computing [101]. Quantum logic clock was the
first with clock uncertainty below $10^{-18}$ [112]. Quantum clocks can also
benefit from quantum entanglement [113].

Later, the quantum logic clock was superseded by experimental optical lattice
clocks. Note that the current atomic clocks work with microwave frequencies,
i.e. the transition between energy levels emits a microwave photon. The
measurement of level transition with the emitted photon in optical frequencies
is harder to achieve, although it offers better performance. Optical clocks
are still in development, with systems being based on: single ions isolated in
an ion trap, neutral atoms trapped in an optical lattice and atoms packed in a
3D quantum gas optical lattice. The 3D quantum gas optical lattice clocks in
particular have demonstrated frequency precision $2.5\times 10^{-19}$ [114].
Recently, it was demonstrated that quantum entanglement could enhance the
clock stability [115].

Another research focuses on vapour-cell (or gas-cell) atomic clock that
provides chip-size realisation [116]; solid-state (for instance, the NV centre
in diamond) clock [117]; or nuclear clock with a similar principle as
microwave or optical atomic clock, except that it uses nuclear transition
instead of electron transition in an atom’s shell [118], with the potential
for unprecedented performance, outstripping atomic optical clocks [119].

Various clock technologies have their own challenges, such as precise
frequency comb, laser system for control and cooling down and black body
radiation shift (in the case of optical clocks). Also, miniaturisation usually
comes at the cost of lower frequency precision. Another common type of
challenge is the synchronisation of those clocks.

Precise timing is essential for many technologies, such as satellite
navigation, space systems, precise measurement, telecommunication, defence,
network synchronisation, finance industry, energy grid control, and in almost
all industrial control systems. However, very precise timing is crucial for
quantum technologies, especially for quantum sensing and imaging. For
instance, a very high precision clock allows new measurements, such as gravity
potential measurement down to the centimetre level at the Earth’s surface or
searching for new physics.

####  3.4.3 Quantum RF Antenna

  * •

Status:  laboratory prototypes

  * •

Utilisation:  effectiveness

  * •

Timeline expectation: short- to mid-term

  * •

Main challenges:  miniaturisation, cooling

Radio frequency (RF) antennas serve as receivers or transmitters of various
signals. They can be simple dipole antennas to complex AESA999AESA (active
electronically scanned array) module consists of an array of small
transmitters and receivers that are controlled by a computer. Simply put, each
cell of AESA can behave as an independent radar module. modules. Their size
limitation is bounded by the wavelength of the produced or received signal.
For example, a 3 GHz signal has a wavelength of $\sim 10$ cm and the size of
the antenna should be no less than approximately $1/3$ of this wavelength.
This is called the Chu–Harrington limit [120, 121].

Rydberg atoms’ technology allows breaking this limit and having an antenna of
the size of a few micrometres independently on the receiving signal
wavelength. Rydberg atoms are highly excited atoms with a correspondingly
large electric dipole moment, and therefore high sensitivity to external
electric field [122, 123]. Note that Rydberg atoms-based antenna can only
receive a signal.

The recent prototypes of Rydberg atoms-based analyser were demonstrated for
frequencies 0 to 20 GHz for AM or FM radio, WiFi and Bluetooth signals [124].
The combination of more antennas can detect the angle-of-arrival of the signal
[125]. At the laboratory level, Rydberg atoms technology is available
commercially.

Quantum RF receiver as a single cell (for targeted frequency, narrow
bandwidth) or arrayed sensor (broad frequency span) can find its applications
in navigation, active imaging (radar), telecommunications, media receiver or
passive THz imaging.

####  3.4.4 Quantum Imaging Systems

  * •

Status:  laboratory prototypes and proof-of-concept verifications

  * •

Utilisation:  new capability

  * •

Timeline expectation: short- to long term

  * •

Main challenges:  improving resolution, high rate single-photon sources

Quantum imaging systems are a wide area covering 3D quantum cameras, behind-
the-corner cameras, low brightness imaging and quantum radar or lidar (for
quantum radar, see Section 3.4.5).

SPAD (Single Photon Avalanche Detectors) Array is a very sensitive single-
photon detector connected with a pulsed illumination source that can measure
the time-of-flight from source to an object and hence the range of the object.
Then, putting SPAD into an array can work as a 3D camera. SPAD works with the
optical spectrum with extension developed to the near-infrared spectrum.

SPAD array can be used to detect objects out of the line of sight, too (e.g.
hidden behind the corner of a wall). The idea is based on laser and camera
cooperation, where the laser sends a pulse in front (e.g. a spot on the floor)
of the SPAD camera. From the spot, the laser pulse will scatter in all
directions, including behind the corner, where the photons can be reflected to
the spot in front of the SPAD camera and then to the camera. SPAD is sensitive
enough to detect such a three-scattered signal [126].

Quantum ghost imaging [127, 128, 129], also known as coincidence imaging or
two-photon imaging, is a technique that allows imaging an object that is out
of the line of sight of the camera. In the source, two entangled photons are
created, each of a different frequency. The one in the optical frequency is
recorded directly by a high-resolution photon-counting camera. The second
photon having a different frequency (e.g. the infrared) is sent toward the
object. The reflected photon is detected by a single-photon detector (the so-
called ‘bucket’ detector). The image is then created from the correlations
between both photons. The ghost imaging protocol was demonstrated without
quantum entanglement, too (using classical correlation), although with worse
resolution.

Such a schema allows imaging an object at extremely low light levels. Also,
infrared light can better penetrate some environments with a better signal-to-
noise ratio (SNR) [130]. Ghost imaging experiments that use x-ray or ultra-
relativistic electrons were demonstrated recently [131, 132].

Sub-shot-noise imaging [133] is another quantum optics schema allowing
detection of a weak absorption object with a signal below the shot noise. Shot
noise is the result of fluctuations in the detected number of photons. For
example, the shot noise is the limit for lasers. This limit can be overcome
using correlated photons. The detection of one ‘herald’ or ‘ancilla’ photon
signifies the presence of the correlated photon that probes the object or
environment.

Quantum Illumination (QI) [134] is a quantum protocol to detect a target using
two correlated (entangled) photons. One photon, called the ‘idler’, is kept.
The other, called the ‘signal’ photon, is sent toward the target and
reflected, and both photons are measured. The advantage of this protocol
remains even when the entanglement is destroyed by a lossy and noisy
environment. QI protocol is one of those mainly adapted for the quantum radar,
but it can also be applied to medical imaging or quantum communication.

####  3.4.5 Quantum Radar Technology

  * •

Status:  laboratory prototypes and proof-of-concept verification

  * •

Utilisation:  new capability

  * •

Timeline expectation: long-term and more

  * •

Main challenges:  high rate single-photon source, quantum microwave technology

Quantum radar, in principle, works similarly to classical radar, in the sense
that a signal has to be sent toward the target, and the radar system needs to
wait for the reflected signal. Nevertheless, theoretically improved precision
and new capabilities can be achieved by quantum mechanical approaching.

There are several protocols considered for quantum radar, such as
interferometric quantum radar [135], quantum illumination (QI) [134], hybrid
quantum radar [136, 137] or Maccone-Ren quantum radar [138]. None of the
mentioned protocols is perfect. Interferometric quantum radar, for example, is
too sensitive to noise and requires quantum entanglement preservation. QI is
an ideal protocol for a noisy environment and is even laboratory-verified for
microwave spectrum [139], but it requires knowledge of the distance to the
target, and such as it has no ranging function. Nevertheless, the QI-based
approach to quantum target ranging is under development [140]. This ranging
problem is also solved by the hybrid quantum radar, but at the expense of
sensitivity. The Maccone-Ren protocol has QI properties and ranging function,
but it is only a theoretical concept so far.

The biggest challenge common to all protocols is the high rate of generation
of entangled photons in (not only) a microwave regime. The quantum version of
the radar equation [141] still holds the dominant term $1/R^{4}$, where $R$ is
the radar–target distance. As a result, the number of demanded entangled
photons (modes) is several orders of magnitude higher than is available
currently [142]. In a sense, quantum radar is similar to noise radars and
shares many properties such as the probability of interception, low
probability of detection, efficient spectrum sharing, etc., see [137] and
references therein.

Another related challenge is target finding. Theoretical work [143] shows that
quantum entanglement can outperform any classical strategy in finding the
unknown position of the target. Moreover, the presented method can work as a
quantum-enhanced frequency scanner for the fixed target range.

####  3.4.6 Other Sensors and Technology

  * •

Status:  laboratory prototypes

  * •

Utilisation:  new capability (e.g. chemical and precise acoustic detection)

  * •

Timeline expectation: short- to medium term

  * •

Main challenges:  improving resolution

Quantum technologies can be used for ultra-precise sound sensing up to the
level of a phonon, a quasiparticle quantising sound waves in solid matter
[144, 145], using photoacoustic detection. Precise detection of acoustic waves
is essential for many applications, including medical diagnostics, sonar,
navigation, trace gas sensing and industrial processes [146, 147].

Photoacoustic detection can be combined with quantum cascade laser and used
for gas or general chemical detection. Quantum cascade laser (QCL) is yet a
mature technology [148]. QCL is a semiconductor laser that emits in the mid-
and long-wave IR bands and, as with many other quantum technologies, requires
cooling far below $-70^{\circ}$C. However, recent development allows chip-
level implementation working at around $-23^{\circ}$C, which can be achieved
by a portable cooling system [149].

##  4 Quantum Technology In Defence

Military technologies have more demanding requirements than industrial or
public applications. This requires greater caution, considering possible
deployment on the battlefield. Sec. 5 presents various possible military
applications with different TRLs, time expectations and with multiple risks of
realisation.

It will be simpler and less risky for technologies that are easily implemented
and fit into current technologies, such as quantum sensors where, simply put,
we can replace a classical sensor with a quantum sensor.

On the contrary, QKD is an example of a technology that is already
commercially available but is challenging to deploy. A lot of new hardware,
systems and interoperability with current communication systems are needed.
Thus, this technology carries more significant risks in terms of military
deployment.

We can expect an advantage in lowering SWaP and scaling up quantum computers
and quantum networks in the long term. That will make the deployment easier
and probably necessary if the nation/army wants to compete with other
nations/armies with edge (quantum) technologies.

###  4.1 Quantum Strategy

The future users of military quantum technologies will have to think carefully
about whether, where and when to invest time and resources. The goal of the
defence forces is not to develop military technology but usually only to
specify requirements and their acquisition. However, they can participate
significantly in development, especially if they are the end user.

As a foundation, it is ideal to have a national quantum ecosystem in place
composed of industry and academic institutions. Such an ecosystem should be
supported generally at the government level, i.e. having a national quantum
plan, but should also be motivated to develop technologies for the defence
sector. This can be achieved through appropriate grant funding and even
various thematic challenges, in which individuals and startups can participate
and perhaps bring new disruptive ideas and solutions. This will naturally lead
to closer cooperation with industry and academia. The quantum industry is
quite interesting, where there is a great deal of cooperation between academia
and industry.

The first step is to establish a quantum technology roadmap or quantum
strategy. The roadmap/strategy should specify all the next steps, from
identifying disruptive quantum solutions, market survey, technology and risk
assessment and development itself to prototype testing and eventually solution
deployment. The roadmap or quantum strategy can consist of three parts:

  1. 1.

Identification,

  2. 2.

Development,

  3. 3.

Implementation and deployment.

The most critical part is the identification of the most advantageous and
disruptive quantum technologies for the considered warfare domains. This step
also includes the technological and scientific assessment to balance
technological risk (limited deployability, performance below expectations, or
impossibility of transfer from the laboratory to the battlefield) versus the
potential advantage of individual quantum technologies. This process of
identification should be repeated in cycles in order to react relatively
quickly to new discoveries and disruptive solutions. It is important to
remember that many applications are yet to be identified or discovered.

The next step is the usual process of research and development (R&D). The R&D
should be sufficiently supported financially, but also with minimal
bureaucratic obstacles. It should involve fast development cycles with close
interaction with the end user of the military technology (specifications and
performance consultations, prototype testing, preparing for certifications,
…). At the end of this phase, the new system at the initial operating
capability should be ready.

The last step is to reach full operational capability, including modification
or creation of new military doctrines, preparing new military scenarios,
strategies and tactics fully exploiting the quantum advantage.

The final note pertains to the Identification phase. Here, the decision maker
needs to also assume the long-term perspective. So far, many quantum
technologies have been considered individually: sensors, QKD, quantum
computing, etc. However, the long-term vision considers the interconnection of
quantum sensors and quantum computing via the quantum network. Here, the
theoretical and experimental works demonstrate additional quantum advantage
exploiting quantum entangled sensors and computers [77, 78]. More similar
applications may yet be discovered or invented. This is important to consider
when the optical-fibre/quantum networks are being built. Later, the current
elements such as trusted repeater can be replaced by fully quantum repeaters
and switches, allowing to reach the full potential of the quantum network.

###  4.2 TRL and Time Horizon

As has been mentioned several times, various quantum technologies are at
different TRL, varying from 1 to 8. The TRL variation and time horizon
expectations are even more complex when considering various applications and
deployment platforms, especially for military purposes. Some TRL and time
horizon estimates were provided in [150]. However, some estimations, such as
quantum precision navigation at TRL 6, seem too optimistic based on what is
described in this report.

Here, we provide our own TRL and expected time horizon in Tab. 1, which
correspond to the findings of this work.

Technology | TRL | Horizon
---|---|---
Quantum computer (annealer) | 4-5 (5-6) | 2030
QKD (satellite) | 7-8 (6-7) | 2025 (2030)
Post-quantum cryptography | 7-8 | 2025
Quantum communication network | 1-3 | 2030-2035
Quantum inertial navigation | 4-5 | 2025-2030
Quantum clocks | 4-6 | 2030
Quantum radar | 1-2 | none
Quantum RF antenna | 4 | 2025-2030
Quantum magnetic and gravity sensing | 5-6 | 2025
Quantum imaging | 5 | 2025-2030
Table 1: TRL and time horizon expectations. These expectation reflect general
TRL rather than just military TRL. Note that various quantum technologies are
at different TRL within the same application.

The reader can compare these with other timelines in [11, 150].

The actual military deployment can take some time to overcome all
technological obstacles and meet military requirements. Take, for example, the
quantum gravimeter for undeground scanning. The first generation will likely
be deployed as a static sensor placed on a truck, and the range/spatial
resolution will be rather low. In time, the next generation will improve
sensitivity and spatial resolution. Along with reduction of SWaP, the sensor
will be capable of being placed aboard an aircraft, and later on a drone and
maybe on an LEO satellite. However, it is also possible that the sensor’s
limits will be reached earlier, resulting in deployment becoming impossible,
e.g. on a drone or LEO satellite.

###  4.3 Quantum Technology Countermeasures

A standalone section on quantum technology countermeasures is warranted,
although this topic will be touched upon, e.g. in Sec. 5.6 about the quantum
analogy of the classical electronic warfare. This topic is less studied, and
few texts deal with this subject; besides, a detailed description is beyond
the scope of this report.

Briefly, this topic refers to the methods and techniques of spoofing,
disabling or destroying quantum technologies, whether it is quantum computers,
quantum networks or quantum sensors and imaging systems. Quantum technologies
exploit the quantum-physical properties of individual quanta. As such, they
are very susceptible to interference and noise from the environment, and so
can potentially be spoofed or paralysed. Especially in relation to quantum
networks and in particular to QKD, we speak about quantum hacking [151, 152,
153, 154, 155], which has developed hand in hand with QKD itself.

Authors and decision makers on quantum strategy should keep in mind that when
quantum technologies are deployed in the military field, various
countermeasures will very likely emerge sooner or later. What is currently
unknown is the possible effectiveness of quantum technology countermeasures
and their impact.

##  5 Quantum Technology Military Applications

Figure 1: Sketch of quantum warfare utilising various quantum technology
systems.

Quantum technologies have the potential to significantly affect many areas of
human activity. This is especially true for the defence sector. Quantum
technologies can impact all the domains of modern warfare. The second quantum
revolution will improve sensitivity and efficiency, and introduce new
capabilities and sharpen modern warfare techniques rather than lead to new
types of weapons.

The following text maps the conceivable quantum technology applications for
military, security, space and intelligence in different aspects of modern
warfare. It also mentions the industrial applications which may suggest
quantum technologies’ capabilities and performances, especially when no public
information on military applications is available.

It is important to notice that many applications are still more theoretical
than realistic. The significant quantum advancement achieved in the laboratory
does not always result in similar progress outside the laboratory. The
transfer from laboratory to practical deployment involves other aspects too,
such as portability, sensitivity, resolution, speed, robustness, low SWaP
(size, weight and power) and cost, apart from a working laboratory prototype.
The practicality and cost-effectiveness of quantum technologies will determine
whether particular quantum technologies are manufactured and deployed.

The integration of quantum technology into a military platform is even more
challenging. Apart from quantum computers that will mostly be located at data
centres similarly as for civil use, the integration and deployment of quantum
sensing, imaging and networks faces several challenges posed by the increased
demands of military use (in comparison with civil/industry or scientific
requirements). For example, the military level requirement of precise
navigation necessitates fast measurement rates that can be quite limiting for
the current quantum inertial sensors. There are more examples, and probably
more are yet to come.

Moreover, this area is still very young, and new technological surprises, both
in a bad and a good sense, could impose other quantum advantages or
disadvantages.

###  5.1 Quantum Cybersecurity

Key points:

  * •

Necessity of quantum crypto-agility implementation.

  * •

Operations that want to take advantage of Shor’s algorithm should start to
collect the data of interest before the quantum-safe encryption is deployed.

  * •

The implementation of QKD needs to be carefully considered.

  * •

In QKD, the endpoints will be the weakest part of the system.

Quantum advantage in cyber warfare can provide new, but on the one hand very
effective (with exponential speedup), vectors of attack on the current
asymmetric encryptions (based on integer factorisation, the discrete logarithm
or the elliptic-curve discrete logarithm problem) and, theoretically, on
symmetric encryption [156, 90]. On the other side are new quantum-resilient
encryption algorithms and approaches, as well as quantum key distribution. For
an overview, see, for example, [157, 158, 159, 160].

The current trend also is the development and employment of machine learning
or artificial intelligence for cyber warfare [161]. For more details on the
quantum opportunities, see Section 5.2.

####  5.1.1 Quantum Defence Capabilities

The post-quantum cryptography implementation is the ‘must-have’ technology
that should be carried out as soon as possible. The risk that hostile
intelligence is gathering encrypted data with the expectation of future
decryption using the power of quantum computers is real, high and present
[162]. This applies to military, intelligence and government sectors as well
as to industry or academia where secrets and confidential data are exchanged
or stored. The current trend is to start preparing the infrastructure for
implementing quantum crypto-agility when the certified (standardised) post-
quantum cryptography becomes ready to deploy [156, 90].

New quantum-resilient algorithms can offer not only a new mathematical
approach difficult enough even for quantum computers, but also a new paradigm
of working with encrypted data. For instance, fully homomorphic encryption
(FHE) allows the data to never get decrypted—even if they are being processed
[163]. Although the security applications, such as for genomic data, medical
records or financial information, are the most mentioned, applications for
intelligence, military or government are evident, too. As such, FHE is a good
candidate for cloud-based quantum computing to ensure secure cloud quantum
computation [164].

Note that post-quantum cryptography should be implemented in the Internet of
Things (IoT), or the Internet of Military Things (IoMT) [165], as a rapidly
growing sector with many potential security breaches. For an overview of post-
quantum cryptography for IoT, see [166].

Quantum key distribution (QKD) [167, 168, 160] is another new capability that
allows safe encryption key exchange where the security is mathematically
proven. Although it is impossible to eavesdrop on the quantum carrier of the
quantum data (key), the weaknesses can be found at the end nodes and trusted
repeaters, due to imperfect hardware or software implementation. Another
question is the cost, considering the quantum data throughput, security and
non-quantum alternatives independently if the solution is optical fibre-based
or utilising quantum satellites. The QKD solution seems to be preferred in EU
[169], while the post-quantum encryption solution finds favour in US [170].

The last note refers to quantum random number generators. QRNG increases
security [171] and denies attacks on pseudorandom number generators [172].

####  5.1.2 Quantum Attack Capabilities

With Shor’s algorithm-based quantum cryptoanalysis of Public key encryption
(PKE)—for instance, RSA, DH, ECC—the attacker can decrypt the encrypted data
collected earlier. There is no precise forecast when the so-called ‘Q-Day’,
the day when a quantum computer breaks the 2048-bit RSA encryption, will
happen. However, the general opinion is it will take about 10–15 years (based
on a survey in 2017) [173]. A similar threat applies to most message
authentication codes (MAC) and authenticated encryption with associated data
(AEAD), such as HMAC-CBC and AES-GCM, because of Simon’s algorithm and
superposition queries.

One has to assume that such offensive operations already exist or that intense
research is being done. In 10 years, most sensitive communication or subjects
of interest will be using the post-quantum cryptography or QKD implemented in
the next six years. That means by the time a quantum computer able to crack
PKE becomes available, most of the security-sensitive data will be using a
quantum-safe solution.

In theory, Grover’s algorithm weakens the symmetric key encryption algorithms;
for example, DES and AES. However, the quantum computing, and in particular
quantum memory, requirements are so huge that it seems to be unfeasible in the
next few decades [174].

Another vector of attack uses the classical hacking methods of classical
computers that will remain behind quantum technologies. In general, quantum
technology is a technologically young sector where plenty of new quantum
system control software is being developed. The new software and the hardware
tend to have more bugs and security breaches. For example, the current QKD
quantum satellites working as trusted repeaters controlled by a classical
computer can be an ideal target for a cyber attack. Moreover, specific
physical-based vectors of attack against quantum networks (e.g. QKD) are the
subject of active research [175], such as photon-number-splitting [81] or the
Trojan-horse attack [82], and future surprises cannot be excluded. For an
overview of quantum hacking, see, e.g. [157].

###  5.2 Quantum Computing Capabilities

Key points:

  * •

Quantum computing capabilities will increase with the number of logical
qubits.

  * •

Most likely, quantum computing will be used as part of a hybrid cloud.

  * •

Small, embedded quantum computing systems are desirable for direct quantum
data processing.

  * •

General use for quantum optimisations, ML/AI enhancements and faster numerical
simulations.

Quantum computing will introduce new capabilities to the current classical
computing services, helping with computational problems of high complexity.
Further, besides the quantum simulations described above, quantum computing
covers quantum optimisations, machine learning and artificial intelligence
(ML/AI) improvement, quantum data analysis, and faster numerical modelling
[24, 11]. The military problems that could be solved with near-term quantum
computers were presented in [10]. They are: Battlefield or war simulations;
Analysis of radio frequency spectrum; Logistics management; Supply chain
optimisation; Energy management; and Predictive maintenance.

To get the most effective results, future quantum computing implementation
will be in computing farms along with classical computers, which will create a
hybrid system. A hybrid quantum-classical operating system will analyse the
tasks to be computed using ML/AI, and split individual computations into
resources such as CPU, GPU, FPGA101010Central Processing Unit; Graphics
Processing Unit; Field-Programmable Gate Array, or quantum processor (QPU),
where the best and fastest result can be obtained.

A small, embedded quantum computer that could be placed, for example, in an
autonomous vehicle or mobile command centre is questionable. The current most
advanced qubit designs need cryogenic cooling. Therefore, more efforts should
be focused on the other qubit designs as photonic, spin or NV centres that can
work at room temperature. The embedded quantum chip could perform simple
analytical tasks or serve for simple operations related to quantum network
applications where a straightforward quantum data process is desired.
Nevertheless, the machine learning and model optimisation of autonomous
systems and robotics can also benefit from ‘large’ quantum computers.

Quantum computing is likely to be efficient in optimisation problems [176,
177, 10]. In the military sector, examples of quantum optimisations could be
logistics for overseas operations and deployment, mission planning, war games,
systems validation and verification, new vehicles’ design and their attributes
such as stealth or agility. At the top will be an application for enhanced
decision making, supporting military operations and functions through quantum
information science, including predictive analytics and ML/AI [178].
Specifically, quantum annealers have proven themselves in verifying and
validating complex systems’ software code [179, 180].

Quantum computers are expected to play a significant role in Command and
Control (C2) systems. The role of C2 systems is to analyse and present
situational awareness or assist with planning and monitoring, including
simulation of various possible scenarios to provide the best conditions for
the best decision. Quantum computers can improve and speed up the scenario
simulations or process and analyse the Big Data from ISR (Intelligence,
Surveillance and Reconnaissance) for enhanced situational awareness. This also
includes the involvement of quantum-enhanced machine learning and quantum
sensors and imaging.

Quantum information processing will probably be essential for Intelligence,
Surveillance, and Reconnaissance(ISR) or situational awareness. ISR will
benefit from quantum computing, which offers a considerable boost to the
ability to filter, decode, correlate and identify features in signals and
images captured by ISR. Quantum image processing in particular is an area of
extensive interest and development. It is expected that in the near term
situational awareness and understanding can benefit from quantum image
analysis and pattern detection utilising neural networks [13].

Quantum computing will enhance classical machine learning and artificial
intelligence [54], including for defence applications [178]. Here, quantum
computing will surely not be practical to carry out the complete machine
learning process. Nevertheless, quantum computing can improve ML/AI machinery
(e.g. quantum sampling, linear algebra, quantum neural networks). A recent
study [181] shows that quantum ML provides an advantage just for some kernels
fitting particular problems. Quantum computing can possibly enhance, in
principle, most classical ML/AI applications in defence; for example,
automating cyber operations, algorithmic targeting, situation awareness and
understanding and automated mission planning [182, 183]. The most immediate
application of quantum ML/AI is probably quantum data; for instance, data
produced by quantum sensing or measuring apparatus [55]. Actual applicability
will grow with quantum computer resources, and in eight years, quantum ML/AI
can be one of the important quantum computing applications [184]. Such
applicability can be accelerated by hybrid classical-quantum machine learning
where tensor network models could be implemented on small near-term quantum
devices [185].

Quantum computers, through quantum neural networks, can be expected to provide
superior pattern recognition and higher speed. This may be essential, for
instance, in bio-mimetic cyber defence systems that protect networks,
analogously to the immune systems of biological organisms [13].

Besides, through faster linear algebra (see 3.2.5), quantum computing has the
potential to improve the current numerical linear equation-based numerical
modelling in the defence sector, such as war games simulations, radar cross
section calculations, stealth design modelling, etc.

In the long term, the quantum systems can enable Network Quantum Enabled
Capability (NQEC) [13]. NQEC is a futuristic system that allows communication
and sharing information across the network between individual units and the
commander to respond quickly to battlefield developments and for coordination.
Quantum enhancement can bring secured communication, enhanced situational
awareness and understanding, remote quantum sensor output fusing and
processing, and improved C2.

###  5.3 Quantum Communication Network

Key points:

  * •

Various security applications (e.g. QKD, identification and authentication,
digital signatures).

  * •

The adoption of security applications will happen as quickly as all new
technology security aspects are explored, carefully.

  * •

Quantum clock synchronisation allows utilising higher precision quantum
clocks.

  * •

Quantum internet is the most effective way of communication between quantum
computers and/or quantum clouds.

Quantum internet stands for a quantum network with various services [186]
which have significant, and not only security, implications. However, many
progressive quantum communication network applications require quantum
entanglement; that is, they require quantum repeater and quantum switch.
Recall that the trusted repeaters can be used for QKD only (see Sec. 3.3.1).
Future combinations of optical fibre and free-space channels will interconnect
various end nodes such as drones, planes, ships, vehicles, soldiers, command
centres, etc.

####  5.3.1 Security Applications

Quantum key distribution is one of the most matured quantum network
applications. This technology is going to be interesting for the defence
sector later, when long-distance communication using MDI-QKD or quantum
repeaters becomes possible. Currently, basic commercial technology that uses
trusted repeaters is available. These pioneers can serve as a model of how
quantum technologies can be employed. Here, QKD companies promote the
technology as the most secure, and more and more use cases appear, especially
in the financial and healthcare sectors. On the other hand, the numerous
recommendation reports and authorities are more circumspect; for example, the
UK National Cyber Security Centre [187] that does not endorse QKD for any
government or military applications in its current state.

Apart from QKD, which distributes the key only, the quantum network could be
used for quantum-secure direct communication (QSDC) [188, 189, 190, 191]
between space, special forces, air, navy and land assets. Here, the direct
messages encrypted in quantum data take advantage of security similar to QKD.
One obstacle could be a low qubit rate, which will only allow sending simple
messages and not audiovisual and complex telemetry data. In that case, the
network switch to the QKD protocol for distributing the key and the encrypted
data will be distributed over classical channels. Other protocols such as
quantum dialogue [192] and quantum direct secret sharing [193] aim to use the
quantum network for provable secure communications as QSDC. Note that QKD and
QSDC are considered to be a native part of 6G wireless communication networks
and discussed accordingly in [194].

Another significant contribution of the quantum approach to security is the
quantum digital signature (QDS) [195]. It is the quantum mechanical equivalent
of a classical digital signature. QDS provides security against tampering of a
message after a sender has signed the message.

Next, quantum secure identification exploits quantum features allowing
identification without revealing authentication credentials [72]. Non-quantum
identification is based on the exchange of login and password or cryptographic
keys, which allows intruders to at least guess who has tried to authenticate.

The other application is position-based quantum cryptography [196, 197].
Position-based quantum cryptography can offer more secure communication, where
the accessed information will be available only from a particular geographical
position, such as communication with military satellites only from particular
military bases. Position-based quantum cryptography can also provide secure
communication when the geographical position of a party is its only
credential.

####  5.3.2 Technical Applications

Quantum network will perform network clock synchronisation [71, 198] that is
already a major topic in classical digital networks. Clock synchronisation
aims to coordinate otherwise independent clocks, especially atomic clocks
(e.g. in GPS) and local digital clocks (e.g. in digital computers). A quantum
network that uses quantum entanglement will reach even more accurate
synchronisation, especially when quantum clocks come to be deployed (for Time
standards and frequency transfer see Sec. 5.4). Otherwise, the high precision
of quantum clocks would be utilised locally only. Precise clock
synchronisation is essential for the cooperation of C4ISR (Command, Control,
Communications, Computers, Intelligence, Surveillance and Reconnaissance)
systems for accurate synchronisation of various data and actions across radar,
electronic warfare, command centres, weapon systems, etc.

A short note is dedicated to blind quantum computing [69, 70]. This class of
quantum protocols allows for a quantum program to run on a remote quantum
computer or quantum computing cloud and retrieve results without the owner
knowing what the algorithm or result was. This is valuable when secret
computation is needed (e.g. military operation planning or new weapon
technology design) and no own quantum computer capability is available.

Distributed quantum computing via the quantum network—see Section 3.3.1—will
be important for the military and governmental actors owning quantum
computers, to build high-performance quantum computing services or quantum
cloud.

A quantum network capable of distributing entanglement can integrate and
entangle quantum sensors [77] for the purpose of improving the sensitivity of
the sensors, reducing errors, and most importantly to perform a global
measurement. That provides an advantage in cases where the parameters of
interest are global properties of the entire network; for example, when a
signal’s angle of arrival needs measurement from three sensors, where each
measures a signal with a certain amplitude and phase. Afterwards, each
sensor’s output can be used to estimate the angle of arrival of the signal.
Quantum entangle sensors can evaluate this globally. This process can then be
improved by machine learning [78].

Quantum protocols for distributed computing agreement [76] can have
advantageous military application for a swarm of drones, or in general for a
herd of autonomous vehicles (AVs). Here, quantum protocols can help achieve
agreement between all AVs at the same time scale, independent of their
quantity. Nevertheless, open space quantum communication between all rapidly
moving AVs will be a challenge that has to be solved first. Note that the
first experiment of quantum entanglement distribution from a drone was
successfully carried out, recently [64].

###  5.4 Quantum PNT

Key points:

  * •

All quantum PNT technologies have in common the demand for a highly accurate
quantum clock.

  * •

Quantum inertial navigation could bring few orders of magnitudes higher
precision than its classical counterpart.

  * •

Quantum inertial navigation can be extended by the quantum augmented
navigation using quantum magnetic or gravity mapping.

  * •

Promising quantum navigation based on Earth’s magnetic anomalies.

Quantum technologies are expected to significantly improve positioning,
navigation and timing (PNT) systems, especially inertial navigation. Time
standards and frequency transfer (TFT) is a fundamental service that provides
precise timing for communication, metrology, but also global navigation
satellite system (GNSS). Although present TFT systems are well established,
the performance of optical atomic or quantum clocks in combination with TFT
utilizing quantum networks [199, 200] will keep pace with the increasing
demands of the present applications (communication, GNSS, financial sector,
radars, electronic warfare systems) and enables new applications (quantum
sensing and imaging).

New quantum-based technologies and approaches support the development of
sensitive precision instruments for PNT. The quantum advantage will be
manifested for GPS denied or challenging operational environments, enabling
precise operations. Examples of such environments are underwater and
underground, or environments under GPS jamming.

Current GNSS (GPS, GLONASS, Galileo, BeiDou, …) rely on precise timing
provided through multiple atomic clocks in individual satellites that are
corrected by the more stable atomic clocks on the ground. The higher precision
of the quantum clock will increase the accuracy of positioning and navigation
as well. Over the long term, the GNSS satellites should be connected to the
quantum internet for timing distribution and clock synchronisation. Chip-size
precise mobile clocks could help discover GNSS deception and spoofing [201].

Some quantum GNSS (not only quantum clock) have been considered and
investigated; for instance, interferometric quantum positioned system (QPS)
[199, 202, 203]. One of the schemes of QPS [202, 203] has a structure similar
to the traditional GNSS where there are three baselines, each consisting of
two low-orbiting satellites, with the baselines are perpendicular to each
other. However, although theoretically the accuracy of positioning is
astonishing, significant engineering must be done to design a realistic QPS.

Most of the current navigation relies on GPS, or in general GNSS, which is the
most precise available technology for navigation. GNSS technology is prone to
jamming, deception, spoofing or GPS-deprived environments such as densely
populated areas with high electromagnetic spectrum use. Moreover, for
underground or underwater environments, GNSS technology is not available at
all. The solution is inertial navigation. The problem with classical inertial
navigation is its drifting, a loss of precision over time. For example, the
marine-grade inertial navigation (for ships, submarines and spacecraft) has a
drift $1.8$ km/day and navigation grade (for military aircraft) has a drift
1.5 km/hour [204]. In 2014, DARPA started a MTO-PTN project with a goal to
reach drift 20 m and 1 ms/hour [205]. Even so, some expectations are very
high, that quantum inertial navigation will offer error of only approximately
hundreds of meters per month [5, 206].

The full quantum inertial navigation system consists of a quantum gyroscope,
accelerometer and atomic/quantum clocks. Although the individual sensors
required for quantum inertial navigation are tested out of laboratories, it is
still challenging to create a complete quantum inertial measurement unit. For
navigation for highly mobile platforms, sensors need fast measurement rates of
several 100 Hz, or to improve the measurement bandwidth of quantum sensors
[204, 207]. The key component that needs the most improvement is the low-drift
rotation sensor. The classical inertial sensors are based on various
principles [208]. One common chip-size technology is the MEMS (Micro Electro
Mechanical Systems) technology, where MEMS gyroscopes have demonstrated
instabilities at level $\sim 10^{-7}$ rad$\cdot$s-1 that is suitable for
military applications [99]. The instability limit for the best current cold-
atom gyroscopes is about $\sim 10^{-9}-10^{-10}$ rad$\cdot$s-1 (at integration
time 1000 s) [209]. The uncertainty is in the precision of the in field-
deployable quantum sensors in comparison to the presented laboratory
experiments’ precision. The intermediate step between classical and quantum
inertial navigation can be a hybrid system fusing the outputs of classical and
quantum accelerometers [210]. With the size of the quantum inertial navigation
device decreasing to chip size, its deployment can be expected on smaller
vehicles, especially unmanned autonomous vehicles or missiles. However, the
miniaturisation we can reach is unknown. There are many doubts about chip-
sized quantum inertial navigation. It is certainly a next-generation
technology, although a very big challenge.

Currently, the individual elements, such as gyroscope or accelerometer, are
also tested on various platforms; for instance, on board an aircraft [211], or
more recently a [212].

For many years, the US National Oceanic and Atmospheric Administration (NOAA)
were mapping the Earth’s magnetic anomaly and creating a magnetic anomalies
map. Using sensitive quantum magnetometers in combination with Earth’s
magnetic anomaly map is another way to realise quantum non-GNSS navigation
[213, 214].

Gravitational map matching [215] works on a similar principle, and one can
expect improved performance using the quantum gravimeter. Together, quantum
gravimeter and magnetometer could be a basis for a submarine quantum augmented
navigation, especially in undersea canyons, wrinkled seabeds, or littoral
environments.

In general, quantum inertial navigation or augmented navigation has vast
potential, since there is no need for GPS, infra or radar navigation and it is
not susceptible to jamming, or in general to electronic warfare attacks.
However, the claim of ‘no need for GPS’ is not quite accurate. These systems
will always need some external input on their initial position, most probably
from GNSS.

###  5.5 Quantum ISTAR

Key points:

  * •

Intense involvement of quantum computing to gather and process information.

  * •

Desired deployment on low-orbit satellites, but the resolution is
questionable.

  * •

Vast applications for undersea operations.

  * •

Expected advanced underground surveillance with uncertain resolution.

  * •

New type of 3D, low-light or low-SNR quantum vision devices.

ISTAR (intelligence, surveillance, target acquisition and reconnaissance) is a
crucial capability of a modern army for precise operations. Quantum
technologies have the potential to dramatically improve situational awareness
of multi-domain battlefields.

In general, a large impact can be expected from quantum computing that will
help with acquiring new intelligence data, processing Big Data from
surveillance and reconnaissance and identifying targets using quantum ML/AI
[178, 183].

Apart from the processing part of ISTAR, dramatic advancement can be expected
from quantum sensing placed on individual land/sea/aerial vehicles and low-
orbit satellites.

Quantum gravimeters and gravitational gradiometers promise high accuracy that
can improve or introduce new applications: geophysics study, seismology,
archaeology, minerals (fissile material or precious metals) and oil detection,
underground scanning and precise georeferencing and topographical mappings
(e.g. of the seabed for underwater navigation) [7].

Another significant type of sensing is quantum magnetometry. The applications
of quantum magnetometry are partially overlapped by applications for quantum
gravimetry, thus introducing new applications: Earth’s magnetic field
including magnetic anomalies, local magnetic anomalies due to the presence,
such as metallic objects (submarines, mines, etc.), or weak biological
magnetic signals (applications mainly for medical purposes) [7].

The third field interesting for ISTAR is quantum imaging. Quantum imaging
offers plenty of diverse applications; for example, quantum radar (see Section
5.7), imaging devices for medicine, 3D camera, stealth rangefinder, etc.

The potential quantum computing applications in ISR and situational awareness
are described in Sec. 5.2.

####  5.5.1 Quantum Earth’s Surface and Underground Surveillance

Quantum sensing based on magnetometry, gravimetry and gravity gradiometry at
the first level helps with the study of continents and sea surface, including
underground changes of natural origin. Both magnetic anomaly and gravity-based
sensing provide a different picture of the Earth’s surface. The Earth is very
inhomogeneous (ocean, rocks, caves, metallic minerals, …), including the
massive constructions or vehicles made by people which generate a unique
gravitational (depending on the mass) and magnetic (depending on metallic
composition) footprint.

The discussed quantum sensing technologies—magnetometry, gravimetry and
gravity gradiometry—can reach very high precision, at least in the laboratory.
For example, the precision of absolute gravimetry out of the laboratory is
about $1~{}\mu$Gal (10 nm$\cdot$s-2) [216]. Note that the sensitivity of
$3.1~{}\mu$Gal corresponds to a sensitivity per centimetre of height above the
Earth’s surface. However, the problem is the spatial resolution that usually
is anti-correlated with the sensitivity (higher sensitivity is at the cost of
lower spatial resolution and vice versa). Spatial resolution and sensitivity
are the critical attributes that define what you will recognise (large-scale
natural changes or small underground structures) and from what distance (from
the ground, drone or satellite-based measurement). Examples of the current
spatial resolution are about 100 km [217] for satellite-borne gravity
gradiometer or 16 km [218] additional width using radar satellite altimetry
(for sea areas), or 5 km [219] for airborne gravimetry. For more information,
see e.g. [5].

For many quantum sensing applications, it would be essential to place sensors
on low Earth orbit (LEO) satellites [220]. However, the current sensitivity
and spatial resolution allow only the applications for Earth monitoring
(mapping resources such as water or oil, earthquake or tsunami detection).

Apart from low-orbit satellites, the mentioned quantum sensors are considered
for deployment on airborne, sea or ground vehicle platforms. Nowadays, quantum
sensing experiments are performed outside the laboratory environment, such as
in a truck [221], on drones and aeroplanes [222, 223] or aboard ships [217].
For example, the quantum gravimeter could be mounted on drones to search for
human-made structures such as tunnels used to smuggle drugs [223]. Placing
quantum sensing devices on a drone (this may be an unmanned aerial vehicle
(UAV), Unmanned Surface Vessel (USV), Remotely Operated Vehicle (ROV) or
unmanned underwater vessel (UUV)) needs more engineering to reach the best
sensitivity, resolution and operability simultaneously.

Low-resolution quantum sensing could be used for precise georeferencing and
topographical mappings to help with underwater navigation or mission planning
in rugged terrain. Also, the detection of new minerals and oil fields can
become a new centre of interest, especially under the seabed [224]. This can
be a source of international friction, despite the fact that borders are clear
in most cases.

High-resolution quantum magnetic and gravity sensing [225, 226, 227, 217] is
considered in numerous reports and articles [7, 228, 229, 230, 231, 225] to be
able to: detect camouflaged vehicles or aircraft; effectively search for a
fleet of ships or individual ships from LEO; detect underground structures
such as caves, tunnels, underground bunkers, research facilities and missile
silos; localise buried unexploded objects (landmines, underwater mines and
improvised explosive devices); achieve through-wall detection of rotating
machinery.

However, note again that it is highly uncertain where the technical limits are
and whether the mentioned quantum gravimetry and magnetometry applications
will reach such sensitivity and resolution (especially for using from LEO) as
to realise all the aforementioned ideas. Quantum sensors will be delivered to
the market in many generations, each with better sensitivity and resolution
and lower SWaP, allowing more extensive deployment and application.

####  5.5.2 Quantum Imaging Systems

Besides quantum radar and lidar (see Section 5.7), there are other military-
related applications of quantum imaging. In general, all-weather, day-night
tactical sensing for ISTAR for long/short-range, active/passive regime,
invisible/stealth using EO/IR/THz/RF frequencies features and advantages are
considered. Quantum imaging systems can use various techniques and quantum
protocols; for example, SPAD, quantum ghost imaging, sub-shot-noise imaging,
or quantum illumination as was described in Section 3.4.4. In general, it is
not a problem to construct quantum imaging systems of small sizes. The
critical parameters are the flux of the single-photon/entangled photon emitter
or the single-photon detection resolution and sensitivity. Moreover, a large-
scale deployment of a quantum imaging system with high photon flux will
require powerful processing that can limit the system deployability and
performance.

Quantum 3D cameras exploiting quantum entanglement and photon-number
correlations will introduce fast 3D imaging with unprecedented depth of focus
with low noise aiming at sub-shot noise or long-range performance. This
capability can be used to inspect and detect deviation or structural cracks on
jets, satellites and other sensitive military technology. Long-range 3D
imaging from UAV can be used for reconnaissance and to explore mission
destination or hostile facilities and equipment.

Another commercially available technology is quantum gas sensors [232].
Technically it is a single-photon quantum lidar calibrated to detect methane
leakage. The next prepared product is a multiple gas detector able to also
detect carbon dioxide (CO2). With proper improvement and calibration, it could
serve for human presence detection, too.

A specific feature at short range is the possibility of behind-the-corner or
out of the line-of-sight visibility, [126]. These methods can help to locate
and recover trapped people, people in hostage situations or to improve
automated driving by detecting incoming vehicles from around a corner.

Quantum imaging can serve as a low-light or low-SNR vision device; for
example, in an environment such as cloudy water, fog, dust, smoke, jungle
foliage or in the nighttime, leading to an advantage. Low-SNR quantum imaging
could help in target detection, classification and identification with low
signal-to-noise ratios or concealed visible signatures and potentially counter
adversaries’ camouflage or other target-deception techniques. Quantum imaging
will be very useful for helicopter pilots when landing in dusty, foggy or
smoky environments [9].

One significant product will be a quantum rangefinder [233, 234]. Conventional
rangefinders use a bright laser and can be easily detected by the target. A
quantum rangefinder will be indistinguishable from the background both
temporally and spectrally when viewed from the target. In other words, the
quantum rangefinder will be invisible and stealthy, including at night time,
whereas the classical rangefinder can be visible to the target or others.

Under some circumstances, quantum ghost imaging can play the role of quantum
lidar [235], especially when the target does not move or moves very slowly and
infinite depth of focus is required for 3D imaging.

###  5.6 Quantum Electronic Warfare

Key points:

  * •

Enhancement of current EW by smaller universal quantum antennas, precise
timing and advanced RF spectrum analysers.

  * •

The problem with detection of quantum channels.

  * •

When the quantum channel is localised, several types of attacks are considered
and developed.

Quantum electronic warfare (EW) can be divided into quantum-enhanced classical
EW and quantum EW focusing on countermeasures, counter-countermeasure and
support against quantum channels. By a quantum channel is meant any transfer
of photons carrying quantum information for quantum internet, quantum radar or
another quantum system that uses the free-space or optical fibres channel.

Classical EW systems for electronic support measures can benefit from the
quantum antenna. Quantum antenna based on Rydberg atoms can offer a small size
independent of the measured signal wavelength (frequency) [122, 123]. This
means that even for low-frequency (MHz to kHz [236, 124]) signal interception
a few-micrometres of quantum antenna is sufficient. There can be an array of
quantum antennas for multi-frequency measurement for different bandwidths or
one antenna dynamically changing bandwidth according to the interest.
Moreover, Rydberg atoms-based antennas can measure both AM and FM signals,
offer self-calibration, and measure both weak and very strong fields and
detect the angle-of-arrival [125]. In the future, quantum antennas could look
like an array (matrix) of Rydberg atom cells. Different cells can measure
different signals, and in the joint measurement of two or more cells, the
angle-of-arrival of the signal could be determined. The weakest aspect of such
antennas is the cryogenics required for cooling Rydberg atoms that need to be
scaled down to an acceptable size. In general, quantum RF sensors are a key
enabler for advanced (LPD/LPI111111Low Probability of Intercept/Low
Probability of Detection (LPI/LPD)) communications, over-horizon directional
RF, resistance to RF interference and jamming, RF direction finding, or RF-THz
imaging. As an example, an arrayed quantum RF sensor is developed as a
potential upgrade for fighter F-35 [237].

Classical EW can also benefit from quantum computing, offering improved RF
spectrum analysers for electronic warfare where quantum optimisations and
quantum ML/AI techniques can be applied. Higher effectiveness can be reached
by the processing and analysing directly of quantum data [55] from RF quantum
sensors (Rydberg atoms, NV centres), where the impact of a quantum computer
can be more significant. Moreover, other quantum-based solutions and
approaches are under development, such as NV centre based RF spectrum analysis
or SHB based rainbow analyser [238].

The current EW systems will also benefit from quantum timing. Quantum timing
can enhance capabilities such as signals intelligence, counter-DRFM (digital
radio frequency memory) and other EW systems that require precise timing; for
instance, counter-radar jamming capabilities.

The other area of quantum EW will be signals intelligence (SIGINT) and
communications intelligence (COMINT) (detecting, intercepting, identifying,
locating) and quantum electronic attack (jamming, deception, use of direct
energy weapons). Quantum channels (for quantum communication or quantum
imaging) have specific characteristics. First, the simple signal interception
is problematic because the quantum data are carried by individual quanta, and
their interception can be easily detected. Second, typical quantum imaging
technologies use a low signal-to-noise ratio, which means that it is
challenging to recognise signal and noise without extra knowledge. Third,
coherent photons, usually used as a signal, behave like a laser that is very
focused. Finding such a quantum signal without knowing the position of at
least one party is very challenging. These characteristics make the classical
EW obsolete and blind against quantum channels.

The situation is difficult even for potential quantum electronic warfare
systems, since it is open to question whether it will be possible to detect
the presence of a quantum (free-space) channel. This will require the
development of quantum analogy of laser warning receivers [239]. For quantum
EW, it will be critical to get intel on the position of one or both parties
using the quantum channel.

Classical EW would intercept and eavesdrop on the free-space classical
channel. However, this is not possible for the quantum channel where it would
be detected promptly. One possible attack is a man-in-the-middle type attack
[240, 241], since the early quantum network parties can have a problem with
authentication or trusted repeaters. Other types of attacks are considered at
the quantum physics level; for example, a photon number splitting attack
relies on utilising coherent laser pulses for the quantum channel [81] or the
Trojan-horse attacks [82], or the collecting of scattered light and its
detection [242]. However, these types of attacks are very sophisticated, and
their practicability, for example in space, is uncertain.

It is more probable that the quantum EW attack will be just a type of denial
of service, where the quantum channel is intercepted, leading to stoppage of
use of the channel. Another possibility is the sophisticated jamming of the
receivers on one or both sides, leading to enormous noise. When the position
of the receiver or transmitter is known, another countermeasure of the
classical EW is to make use of directed energy weapons such as laser, leading
to damage or destruction of sensors. Such an attack also could help
eavesdroppers [155].

In general, new approaches and methods will need to be developed to realise
the capabilities of quantum electronic warfare and address the corresponding
requirements.

###  5.7 Quantum Radar and Lidar

Key points:

  * •

Long-range surveillance quantum radar is unlikely with existing quantum
microwave technology.

  * •

Possible applications in the optical regime - quantum lidar.

  * •

Quantum radar could be used for space warfare.

The perception of the quantum radar topic [141, 243, 244] is affected by the
hype in the media claiming quantum radar development in China [245, 246] or by
optimistic laboratory experiments. Indeed, the theoretical advantages and
features of quantum radar are significant (some of them depend on individual
quantum protocols):

  * -

Higher resistance to noise—that is, better SNR (signal-to-noise ratio)—higher
resistance to jamming and other electronic warfare countermeasures;

  * -

Based on individual photons; that is, the output signal power is so low that
it will be invisible to electronic warfare measures;

  * -

Target illumination; that is, a radar allowing identification of the target.

Based on the list of unique quantum radar features, it could be a powerfully
disruptive technology that could change the rules of modern warfare.
Therefore, attention is being paid to this topic internationally, despite the
immaturity of the technology, and the many doubts about whether the quantum
radar could work as the standard primary surveillance radar.

Moreover, many people immediately imagine quantum radar as a long-range
surveillance radar with a range of hundreds of kilometres, whereas such an
application of quantum radar seems unlikely [247, 248]. Such an optimal, long-
term surveillance quantum radar would be extremely expensive (many orders of
magnitude higher than the classical radar cost for any range) [247], and it
would still not fulfil all the advantages and features listed above.

Briefly, the practical problems are the following [247]. Quantum radar too is
subject to the radar equation, where the received power is lost with the
distance’s fourth power. In parallel, to keep the quantum advantage, it is
desirable to have one or fewer photons per mode. In summary, the relatively
high power made of low-photon modes in the microwave regime is needed to be
generated. This requires a lot of quantum signal generators, cryogenics, large
antenna sizes, etc. All this leads to extremely high cost, and impractical
design [247, 137]. Scientists need to come up with more practical quantum
microwave technology to overcome these difficulties.

Apart from the high price, scepticism also remains about the detection of
stealthy targets or jamming resistance. Quantum radar can be advantageous
against a barrage jammer, but not necessarily against a DRFM or other smart
jammer [247]. In summary, the long-range surveillance quantum radar is
unlikely to be achieved even as a long-term prospect. For its realisation, one
would need to evolve new technology allowing smaller cryogenics, RF quantum
emitter working at a higher temperature or more efficient cryogenics cooling,
and a more powerful emitter (high rate of low photon pulses). Note that even
if the room-temperature superconducting materials were developed, it would not
help in the Josephson parametric amplifier (JPA) method of entangled microwave
photon generation [249]. Nevertheless, JPA is not the only method to obtain
entangled microwave photons [137]. It is not entirely impossible that a new
theory and designs of quantum radar will be discovered in the future. The
long-range surveillance quantum radar described above would suffer from large
size, weight and power consumption, and it is questionable if such a radar
would be stealthy [247].

Another problem is the ranging in the case of quantum illumination (QI)
protocol. QI protocol requires knowledge of the target in advance, and
therefore it requires some extension for ranging, whether classical or quantum
[6].

For several years, it was believed that the quantum radar cross section (RCS)
is higher than the RCS of classical radars [250, 251]. A new precise study of
quantum RCS [252] shows that the previously claimed advantage of quantum RCS
over the classical RCS results from erroneous approximation. Quantum and
classical RCS seem to be equal, at the moment.

Another approach can be the quantum-enhanced noise radar [253, 254, 137].
Noise radar uses noise waveform as a transmission signal, and detection is
based on the correlation between the transmitted signal and the received noise
waveform radar returns. The advantage is the low probability of interception
(LPI), being nearly undetectable by today’s intercept receivers. The quantum
noise radar design needs more study to see practical applicability. However, a
potential use here is especially for the microwave regime.

Still, the current theory and research have applications in the radar sector,
especially that which uses the optical or near-optical photons; that is,
quantum lidar. Here, a short-range quantum lidar could be used for target
illumination at short distances. Experiments with single-photon imaging were
demonstrated from 10 [255] to 45 km [256]. In this range, quantum lidar could
operate as an anti-drone surveillance radar or as part of a SHORAD (Short
Range Air Defense) complex.

Space can be another example of an advantageous environment for quantum
radar/lidar [257] which is low noise for the optical regime, and it even
almost eliminates the decoherence problem in the case of entangled photons.
For example, Raytheon performs simulations of the quantum radar in the optical
regime for space domain [258, 259]. The idea is to place a quantum radar on a
satellite and detect small satellites that are difficult to detect because of
their small cross-sectional area, reflectivity, and environmental lighting
conditions. The deployment of quantum radar/lidar for the space environment
can provide almost all the advantages listed above.

A small note is dedicated here to quantum-enhanced radar. Classical radar can
be equipped with an atomic or quantum clock. Such quantum-enhanced radars show
high precision and reduced noise, and thus demonstrate an advantage in
detecting small, slow-moving objects such as drones [260].

###  5.8 Quantum Underwater Warfare

Key points:

  * •

Submarines can be one of the first adopters of quantum inertial navigation.

  * •

Quantum magnetometers as the main tool for detection of submarines or
underwater mines.

Quantum technologies can significantly interfere in underwater warfare, with
enhanced magnetic detection of a submarine or underwater mines, novel inertial
submarine navigation and quantum-enhanced precise sonars. In general, in the
maritime environment, sensing based on quantum photo-detectors, radar, lidar,
magnetometers, or gravimeters can be applied [257]. For a general overview of
the implications of quantum technology for nuclear weapon submarines’ near
invulnerability, see [261].

Submarines and other underwater vehicles will benefit from quantum inertial
navigation described in Section 5.4 about PNT. Large submarines can probably
be one of the first adopters of quantum inertial navigation because they can
afford to install larger quantum devices, including cryogenics cooling.
Moreover, sensitive quantum magnetometers and gravimeters can help map
surroundings such as an undersea canyon, icebergs and a wrinkled sea bottom
without using sonar that can be easily detected. An example of another type of
inertial navigation especially suitable for underwater arctic navigation is
based on quantum imaging [262].

The basic tool for anti-submarine warfare could be the quantum magnetometer.
Researchers anticipate that the SQUID magnetometers in particular could detect
a submarine from 6 kilometres away, with still improving noise suppression
[263, 264]. Note that the current classical magnetic anomaly detectors,
usually mounted on a helicopter or a plane, have a range of only hundreds of
meters. An array of quantum magnetometers, such as along the coast, could
cover significant areas, leading to denial area for submarines. Moreover, an
array of quantum magnetometers seems to work better with more suppressed
noise.

Quantum magnetometers can also be used to detect underwater mines using, for
instance, an unmanned underwater vessel [230].

However, the main discussion is about the detection range, sensitivity, etc.,
as in Sec. 5.5.1. Even other underwater domain technology such as sonar offers
longer detection range [229]. It was also pointed out in [261] that quantum
technologies will have little impact on SSBN (ballistic missile submarines).
It is possible that quantum magnetometers could work with other sensors to aid
in detection, identification and classification of targets [229].

###  5.9 Quantum Space Warfare

Key points:

  * •

Important for long-distance quantum communication.

  * •

Low Earth orbit will be important for the future deployment of quantum sensing
and imaging technologies.

  * •

Space warfare will lead to new quantum radar/lidar and quantum electronic
warfare technologies for deployment in space.

The space domain is gaining in importance and will be an important battlefield
used by advanced countries. Space used to be a place mainly for satellites for
navigation, mapping, communication and surveillance, often for military
purposes. Nowadays, space is becoming more weaponised [265]; for example,
satellites with laser weapons or ‘kamikaze’ satellites are placed in Earth
orbit, and anti-satellite warfare is growing in parallel. Another surging
problem is the amount of space garbage, with the number of satellites
estimated at 2,200 and several more planned to be released [266].

Space also will be key for placing quantum sensing and communication
technology in satellites [267, 268, 269, 270, 271], as well as for space
countermeasures.

For many quantum technology applications described in previous sections, it
would be desirable to place quantum sensing technology such as quantum
gravimeter, gravity gradiometer or magnetometer on satellites in Earth orbit,
especially the low one (LEO). Such applications are in development; for
example, a low-power quantum gravity sensing device that can be deployed in
space on board a small satellite for accurately mapping resources or to aid in