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A randomized benchmarking suite for mid-circuit measurements

Mid-circuit measurements are a key component in many quantum information computing protocols, including quantum error correction, fault-tolerant logical operations, and measurement based quantum computing. As such, techniques to quickly and efficiently characterize or benchmark their performance are of great interest. Beyond the measured qubit, it is also relevant to determine what, if any, impact mid-circuit measurement has on adjacent, unmeasured, spectator qubits. Here, we present a mid-circuit measurement benchmarking suite developed from the ubiquitous paradigm of randomized benchmarking. We show how our benchmarking suite can be used to both detect as well as quantify errors on both measured and spectator qubits, including measurement-induced errors on spectator qubits and entangling errors between measured and spectator qubits. We demonstrate the scalability of our suite by simultaneously characterizing mid-circuit measurement on multiple qubits from an IBM Quantum Falcon device, and support our experimental results with numerical simulations. Further, using a mid-circuit measurement tomography protocol we establish the nature of the errors identified by our benchmarking suite.

2207.04836v2

Preparations for Quantum Simulations of Quantum Chromodynamics in 1+1 Dimensions: (I) Axial Gauge

Tools necessary for quantum simulations of $1+1$ dimensional quantum chromodynamics are developed. When formulated in axial gauge and with two flavors of quarks, this system requires 12 qubits per spatial site with the gauge fields included via non-local interactions. Classical computations and D-Wave's quantum annealer Advantage are used to determine the hadronic spectrum, enabling a decomposition of the masses and a study of quark entanglement. Color edge states confined within a screening length of the end of the lattice are found. IBM's 7-qubit quantum computers, ibmq_jakarta and ibm_perth, are used to compute dynamics from the trivial vacuum in one-flavor QCD with one spatial site. More generally, the Hamiltonian and quantum circuits for time evolution of $1+1$ dimensional $SU(N_c)$ gauge theory with $N_f$ flavors of quarks are developed, and the resource requirements for large-scale quantum simulations are estimated.

2207.01731v3

Wide Quantum Circuit Optimization with Topology Aware Synthesis

Unitary synthesis is an optimization technique that can achieve optimal multi-qubit gate counts while mapping quantum circuits to restrictive qubit topologies. Because synthesis algorithms are limited in scalability by their exponentially growing run time and memory requirements, application to circuits wider than 5 qubits requires divide-and-conquer partitioning of circuits into smaller components. In this work, we will explore methods to reduce the depth (program run time) and multi-qubit gate instruction count of wide (16-100 qubit) mapped quantum circuits optimized with synthesis. Reducing circuit depth and gate count directly impacts program performance and the likelihood of successful execution for quantum circuits on parallel quantum machines. We present TopAS, a topology aware synthesis tool built with the \emph{BQSKit} framework that preconditions quantum circuits before mapping. Partitioned subcircuits are optimized and fitted to sparse qubit subtopologies in a way that balances the often opposing demands of synthesis and mapping algorithms. This technique can be used to reduce the depth and gate count of wide quantum circuits mapped to the sparse qubit topologies of Google and IBM. Compared to large scale synthesis algorithms which focus on optimizing quantum circuits after mapping, TopAS is able to reduce depth by an average of 35.2% and CNOT gate count an average of 11.5% when targeting a 2D mesh topology. When compared with traditional quantum compilers using peephole optimization and mapping algorithms from the Qiskit or $t|ket\rangle$ toolkits, our approach is able to provide significant improvements in performance, reducing CNOT counts by 30.3% and depth by 38.2% on average.

2206.13645v2

Calculating spin correlations with a quantum computer

We calculate spin correlation functions using IBM quantum processors, accessed online. We demonstrate the rotational invariance of the singlet state, interesting properties of the triplet states, and surprising features of a state of three entangled qubits. This exercise is ideal for remote learning and generates data with real quantum mechanical systems that are impractical to investigate in the local laboratory. Students learn a wide variety of skills, including calculation of multipartite spin correlation functions, design and analysis of quantum circuits, and remote measurement with real quantum processors.

2206.14584v1

Supervised learning of random quantum circuits via scalable neural networks

Predicting the output of quantum circuits is a hard computational task that plays a pivotal role in the development of universal quantum computers. Here we investigate the supervised learning of output expectation values of random quantum circuits. Deep convolutional neural networks (CNNs) are trained to predict single-qubit and two-qubit expectation values using databases of classically simulated circuits. These circuits are represented via an appropriately designed one-hot encoding of the constituent gates. The prediction accuracy for previously unseen circuits is analyzed, also making comparisons with small-scale quantum computers available from the free IBM Quantum program. The CNNs often outperform the quantum devices, depending on the circuit depth, on the network depth, and on the training set size. Notably, our CNNs are designed to be scalable. This allows us exploiting transfer learning and performing extrapolations to circuits larger than those included in the training set. These CNNs also demonstrate remarkable resilience against noise, namely, they remain accurate even when trained on (simulated) expectation values averaged over very few measurements.

2206.10348v2

Quantum Circuit Optimization and Transpilation via Parameterized Circuit Instantiation

Parameterized circuit instantiation is a common technique encountered in the generation of circuits for a large class of hybrid quantum-classical algorithms. Despite being supported by popular quantum compilation infrastructures such as IBM Qiskit and Google Cirq, instantiation has not been extensively considered in the context of circuit compilation and optimization pipelines. In this work, we describe algorithms to apply instantiation during two common compilation steps: circuit optimization and gate-set transpilation. When placed in a compilation workflow, our circuit optimization algorithm produces circuits with an average of 13% fewer gates than other optimizing compilers. Our gate-set transpilation algorithm can target any gate-set, even sets with multiple two-qubit gates, and produces circuits with an average of 12% fewer two-qubit gates than other compilers. Overall, we show how instantiation can be incorporated into a compiler workflow to improve circuit quality and enhance portability, all while maintaining a reasonably low compile time overhead.

2206.07885v1

Preparing Maximally Entangled States By Monitoring the Environment-System Interaction In Open Quantum Systems

A common assumption in open quantum systems in general is that the noise induced by the environment, due to the continuous interaction between a quantum system and its environment, is responsible for the disappearance of quantum properties of this quantum system. Interestingly, we show that an environment can be engineered and controlled to direct an arbitrary quantum system towards a maximally entangled state and thus can be considered as a resource for quantum information processing. Barreiro et.al. [Nature 470, 486 (2011)] demonstrated this idea experimentally using an open-system quantum simulator up to five trapped ions . In this paper, we direct an arbitrary initial mixed state of two and four qubits, which is interacting with its environment, into a maximally entangled state . We use QASM simulator and also an IBM Q real processor, with and without errors mitigating, to investigate the effect of the noise on the preparation of the initial mixed state of the qubits in addition to the population of the target state.

2206.02590v1

Error mitigation for quantum kernel based machine learning methods on IonQ and IBM quantum computers

Kernel methods are the basis of most classical machine learning algorithms such as Gaussian Process (GP) and Support Vector Machine (SVM). Computing kernels using noisy intermediate scale quantum (NISQ) devices has attracted considerable attention due to recent progress in the design of NISQ devices. However noise and errors on current NISQ devices can negatively affect the predicted kernels. In this paper we utilize two quantum kernel machine learning (ML) algorithms to predict the labels of a Breast Cancer dataset on two different NISQ devices: quantum kernel Gaussian Process (qkGP) and quantum kernel Support Vector Machine (qkSVM). We estimate the quantum kernels on the 11 qubit IonQ and the 5 qubit IBMQ Belem quantum devices. Our results demonstrate that the predictive performances of the error mitigated quantum kernel machine learning algorithms improve significantly compared to their non-error mitigated counterparts. On both NISQ devices the predictive performances became comparable to those of noiseless quantum simulators and their classical counterparts

2206.01573v3

Quantum Error Mitigation via Quantum-Noise-Effect Circuit Groups

Near-term quantum computers have been built as intermediate-scale quantum devices and are fragile against quantum noise effects, namely, NISQ devices. Traditional quantum-error-correcting codes are not implemented on such devices and to perform quantum computation in good accuracy with these machines we need to develop alternative approaches for mitigating quantum computational errors. In this work, we propose quantum error mitigation (QEM) scheme for quantum computational errors which occur due to couplings with environments during gate operations, i.e., decoherence. To establish our QEM scheme, first we estimate the quantum noise effects on single-qubit states and represent them as groups of quantum circuits, namely, quantum-noise-effect circuit groups. Then our QEM scheme is conducted by subtracting expectation values generated by the quantum-noise-effect circuit groups from that obtained by the quantum circuits for the quantum algorithms under consideration. As a result, the quantum noise effects are reduced, and we obtain approximately the ideal expectation values via the quantum-noise-effect circuit groups and the numbers of elementary quantum circuits composing them scale polynomial with respect to the products of the depths of quantum algorithms and the numbers of register bits. To numerically demonstrate the validity of our QEM scheme, we run noisy quantum simulations of qubits under amplitude damping effects for four types of quantum algorithms. Furthermore, we implement our QEM scheme on IBM Q Experience processors and examine its efficacy. Consequently, the validity of our scheme is verified via both the quantum simulations and the quantum computations on the real quantum devices.

2205.13907v5

Sample-efficient verification of continuously-parameterized quantum gates for small quantum processors

Most near-term quantum information processing devices will not be capable of implementing quantum error correction and the associated logical quantum gate set. Instead, quantum circuits will be implemented directly using the physical native gate set of the device. These native gates often have a parameterization (e.g., rotation angles) which provide the ability to perform a continuous range of operations. Verification of the correct operation of these gates across the allowable range of parameters is important for gaining confidence in the reliability of these devices. In this work, we demonstrate a procedure for sample-efficient verification of continuously-parameterized quantum gates for small quantum processors of up to approximately 10 qubits. This procedure involves generating random sequences of randomly-parameterized layers of gates chosen from the native gate set of the device, and then stochastically compiling an approximate inverse to this sequence such that executing the full sequence on the device should leave the system near its initial state. We show that fidelity estimates made via this technique have a lower variance than fidelity estimates made via cross-entropy benchmarking. This provides an experimentally-relevant advantage in sample efficiency when estimating the fidelity loss to some desired precision. We describe the experimental realization of this technique using continuously-parameterized quantum gate sets on a trapped-ion quantum processor from Sandia QSCOUT and a superconducting quantum processor from IBM Q, and we demonstrate the sample efficiency advantage of this technique both numerically and experimentally.

2205.13074v3

Multi-state Swap Test Algorithm

Estimating the overlap between two states is an important task with several applications in quantum information. However, the typical swap test circuit can only measure a sole pair of quantum states at a time. In this study we designed a recursive quantum circuit to measure overlaps of multiple quantum states $|\phi_1...\phi_n\rangle$ concurrently with $O(n\log n)$ controlled-swap (CSWAP) gates and $O(\log n)$ ancillary qubits. This circuit enables us to get all pairwise overlaps among input quantum states $|\langle\phi_i|\phi_j\rangle|^2$. Compared with existing schemes for measuring the overlap of multiple quantum states, our scheme provides higher precision and less consumption of ancillary qubits. In addition, we performed simulation experiments on IBM quantum cloud platform to verify the superiority of the scheme.

2205.07171v1

Vector Field Visualization of Single-Qubit State Tomography

As the variety of commercially available quantum computers continues to increase so does the need for tools that can characterize, verify and validate these computers. This work explores using quantum state tomography for characterizing the performance of individual qubits and develops a vector field visualization for presentation of the results. The proposed protocol is demonstrated in simulation and on quantum computing hardware developed by IBM. The results identify qubit performance features that are not reflected in the standard models of this hardware, indicating opportunities to improve the accuracy of these models. The proposed qubit evaluation protocol is provided as free open-source software to streamline the task of replicating the process on other quantum computing devices.

2205.02483v1

Quantum Computing Approaches for Mission Covering Optimization

We study quantum computing algorithms for solving certain constrained resource allocation problems we coin as Mission Covering Optimization (MCO). We compare formulations of constrained optimization problems using Quantum Annealing techniques and the Quantum Alternating Operator Ansatz (Hadfield et al. arXiv:1709.03489v2, a generalized algorithm of the Quantum Approximate Optimization Algorithm, Farhi et al. arXiv:1411.4028v1) on D-Wave and IBM machines respectively using the following metrics: cost, timing, constraints held, and qubits used. We provide results from two different MCO scenarios and analyze results.

2205.02212v1

Analyzing Strategies for Dynamical Decoupling Insertion on IBM Quantum Computer

Near-term quantum devices are subject to errors and decoherence error is one of the non-negligible sources. Dynamical decoupling (DD) is a well-known technique to protect idle qubits from decoherence error. However, the optimal approach to inserting DD sequences still remains unclear. In this paper, we identify different conditions that lead to idle qubits and evaluate strategies for DD insertion under these specific conditions. Specifically, we divide the idle qubit into crosstalk-idle or idle-idle qubit depending on its coupling with other qubits and report the DD insertion strategies for the two types of idle qubits. We also perform Ramsey experiment to understand the reasons behind the strategy choice. Finally, we provide design guidelines for DD insertion for small circuits and insights for large-scale circuit design.

2204.14251v1

Hybrid quantum-classical reservoir computing of thermal convection flow

We simulate the nonlinear chaotic dynamics of Lorenz-type models for a classical two-dimensional thermal convection flow with 3 and 8 degrees of freedom by a hybrid quantum--classical reservoir computing model. The high-dimensional quantum reservoir dynamics are established by universal quantum gates that rotate and entangle the individual qubits of the tensor product quantum state. A comparison of the quantum reservoir computing model with its classical counterpart shows that the same prediction and reconstruction capabilities of classical reservoirs with thousands of perceptrons can be obtained by a few strongly entangled qubits. We demonstrate that the mean squared error between model output and ground truth in the test phase of the quantum reservoir computing algorithm increases when the reservoir is decomposed into separable subsets of qubits. Furthermore, the quantum reservoir computing model is implemented on a real noisy IBM quantum computer for up to 7 qubits. Our work thus opens the door to model the dynamics of classical complex systems in a high-dimensional phase space effectively with an algorithm that requires a small number of qubits.

2204.13951v2

Experimental implementation of quantum algorithm for association rules mining

Recently, a quantum algorithm for a fundamentally important task in data mining, association rules mining (ARM), called qARM for short, has been proposed. Notably, qARM achieves significant speedup over its classical counterpart for implementing the main task of ARM, i.e., finding frequent itemsets from a transaction database. In this paper, we experimentally implement qARM on both real quantum computers and a quantum computing simulator via the IBM quantum computing platform. In the first place, we design quantum circuits of qARM for a 2$\times$2 transaction database (i.e., a transaction database involving two transactions and two items), and run it on four real five-qubit IBM quantum computers as well as on the simulator. For a larger 4$\times$4 transaction database which would lead to circuits with more qubits and a higher depth than the currently accessible IBM real quantum devices can handle, we also construct the quantum circuits of qARM and execute them on "aer\_simulator" alone. Both experimental results show that all the frequent itemsets from the two transaction databases are successfully derived as desired, demonstrating the correctness and feasibility of qARM. Our work may serve as a benchmarking, and provide prototypes for implementing qARM for larger transaction databases on both noisy intermediate-scale quantum devices and universal fault-tolerant quantum computers.

2204.13634v2

Experimental limit on non-linear state-dependent terms in quantum theory

We report the results of an experiment that searches for causal non-linear state-dependent terms in quantum field theory. Our approach correlates a binary macroscopic classical voltage with the outcome of a projective measurement of a quantum bit, prepared in a coherent superposition state. Measurement results are recorded in a bit string, which is used to control a voltage switch. Presence of a non-zero voltage reading in cases of no applied voltage is the experimental signature of a non-linear state-dependent shift of the electromagnetic field operator. We implement blinded measurement and data analysis with three control bit strings. Control of systematic effects is realized by producing one of the control bit strings with a classical random-bit generator. The other two bit strings are generated by measurements performed on a superconduting qubit in an IBM Quantum processor, and on a $^{15}$N nuclear spin in an NV center in diamond. Our measurements find no evidence for electromagnetic quantum state-dependent non-linearity. We set a bound on the parameter that quantifies this non-linearity $|\epsilon_{\gamma}|<4.7\times 10^{-11}$, at 90% confidence level. Within the Everett many-worlds interpretation of quantum theory, our measurements place limits on the electromagnetic interaction between different branches of the universe, created by preparing the qubit in a superposition state.

2204.11875v1

A quantum Fourier transform (QFT) based note detection algorithm

In quantum information processing (QIP), the quantum Fourier transform (QFT) has a plethora of applications [1] [2] [3]: Shor's algorithm and phase estimation are just a few well-known examples. Shor's quantum factorization algorithm, one of the most widely quoted quantum algorithms [4] [5] [6] relies heavily on the QFT and efficiently finds integer prime factors of large numbers on quantum computers [4]. This seminal ground-breaking design for quantum algorithms has triggered a cascade of viable alternatives to previously unsolvable problems on a classical computer that are potentially superior and can run in polynomial time. In this work we examine the QFT's structure and implementation for the creation of a quantum music note detection algorithm both on a simulated and a real quantum computer. Though formal approaches [7] [1] [8] [9] exist for the verification of quantum algorithms, in this study we limit ourselves to a simpler, symbolic representation which we validate using the symbolic SymPy [10] [11] package which symbolically replicates quantum computing processes. The algorithm is then implemented as a quantum circuit, using IBM's qiskit [12] library and finally period detection is exemplified on an actual single musical tone using a varying number of qubits.

2204.11775v2

Quantum Error Detection Without Using Ancilla Qubits

In this paper, we describe and experimentally demonstrate an error detection scheme that does not employ ancilla qubits or mid-circuit measurements. This is achieved by expanding the Hilbert space where a single logical qubit is encoded using several physical qubits. For example, one possible two qubit encoding identifies $|0\rangle_L=|01\rangle$ and $|1\rangle_L=|10\rangle$. If during the final measurement a $|11\rangle$ or $|00\rangle$ is observed an error is declared and the run is not included in subsequent analysis. We provide codewords for a simple bit-flip encoding, a way to encode the states, a way to implement logical $U_3$ and logical $C_x$ gates, and a description of which errors can be detected. We then run Greenberger-Horne-Zeilinger circuits on the transmon based IBM quantum computers, with an input space of $N\in\{2,3,4,5\}$ logical qubits and $Q\in\{1,2,3,4,5\}$ physical qubits per logical qubit. The results are compared relative to $Q=1$ with and without error detection and we find a significant improvement for $Q\in\{2,3,4\}$.

2204.11114v1

IBM quantum platforms: a quantum battery perspective

We characterize for the first time the performances of IBM quantum chips as quantum batteries, specifically addressing the single-qubit Armonk processor. By exploiting the Pulse access enabled to some of the IBM Quantum processors via the Qiskit package, we investigate advantages and limitations of different profiles for classical drives used to charge these miniaturized batteries, establishing the optimal compromise between charging time and stored energy. Moreover, we consider the role played by various possible initial conditions on the functioning of the quantum batteries. As main result of our analysis, we observe that unavoidable errors occurring in the initialization phase of the qubit, which can be detrimental for quantum computing applications, only marginally affects energy transfer and storage. This can lead counter-intuitively to improvements of the performances. This is a strong indication of the fact that IBM quantum devices are already in the proper range of parameters to be considered as good and stable quantum batteries, comparable to state of the art devices recently discussed in literature.

2204.10786v1

Programming Quantum Hardware via Levenberg Marquardt Machine Learning

Significant challenges remain with the development of macroscopic quantum computing, hardware problems of noise, decoherence, and scaling, software problems of error correction, and, most important, algorithm construction. Finding truly quantum algorithms is quite difficult, and many quantum algorithms, like Shor prime factoring or phase estimation, require extremely long circuit depth for any practical application, necessitating error correction. Machine learning can be used as a systematic method to nonalgorithmically program quantum computers. Quantum machine learning enables us to perform computations without breaking down an algorithm into its gate building blocks, eliminating that difficult step and potentially reducing unnecessary complexity. In addition, we have shown that our machine learning approach is robust to both noise and to decoherence, which is ideal for running on inherently noisy NISQ devices which are limited in the number of qubits available for error correction. We demonstrated this using a fundamentally non classical calculation, experimentally estimating the entanglement of an unknown quantum state. Results from this have been successfully ported to the IBM hardware and trained using a powerful hybrid reinforcement learning technique which is a modified Levenberg Marquardt LM method. The LM method is ideally suited to quantum machine learning as it only requires knowledge of the final measured output of the quantum computation, not intermediate quantum states which are generally not accessible. Since it processes all the learning data simultaneously, it also requires significantly fewer hits on the quantum hardware. Machine learning is demonstrated with results from simulations and runs on the IBM Qiskit hardware interface.

2204.07011v2

Ground state preparation and energy estimation on early fault-tolerant quantum computers via quantum eigenvalue transformation of unitary matrices

Under suitable assumptions, the algorithms in [Lin, Tong, Quantum 2020] can estimate the ground state energy and prepare the ground state of a quantum Hamiltonian with near-optimal query complexities. However, this is based on a block encoding input model of the Hamiltonian, whose implementation is known to require a large resource overhead. We develop a tool called quantum eigenvalue transformation of unitary matrices with real polynomials (QET-U), which uses a controlled Hamiltonian evolution as the input model, a single ancilla qubit and no multi-qubit control operations, and is thus suitable for early fault-tolerant quantum devices. This leads to a simple quantum algorithm that outperforms all previous algorithms with a comparable circuit structure for estimating the ground state energy. For a class of quantum spin Hamiltonians, we propose a new method that exploits certain anti-commutation relations and further removes the need of implementing the controlled Hamiltonian evolution. Coupled with Trotter based approximation of the Hamiltonian evolution, the resulting algorithm can be very suitable for early fault-tolerant quantum devices. We demonstrate the performance of the algorithm using IBM Qiskit for the transverse field Ising model. If we are further allowed to use multi-qubit Toffoli gates, we can then implement amplitude amplification and a new binary amplitude estimation algorithm, which increases the circuit depth but decreases the total query complexity. The resulting algorithm saturates the near-optimal complexity for ground state preparation and energy estimating using a constant number of ancilla qubits (no more than 3).

2204.05955v2

Expressivity of Variational Quantum Machine Learning on the Boolean Cube

Categorical data plays an important part in machine learning research and appears in a variety of applications. Models that can express large classes of real-valued functions on the Boolean cube are useful for problems involving discrete-valued data types, including those which are not Boolean. To this date, the commonly used schemes for embedding classical data into variational quantum machine learning models encode continuous values. Here we investigate quantum embeddings for encoding Boolean-valued data into parameterized quantum circuits used for machine learning tasks. We narrow down representability conditions for functions on the $n$-dimensional Boolean cube with respect to previously known results, using two quantum embeddings: a phase embedding and an embedding based on quantum random access codes. We show that for any real-valued function on the $n$-dimensional Boolean cube, there exists a variational linear quantum model based on a phase embedding using $n$ qubits that can represent it and an ensemble of such models using $d < n$ qubits that can express any function with degree at most $d$. Additionally, we prove that variational linear quantum models that use the quantum random access code embedding can express functions on the Boolean cube with degree $ d\leq \lceil\frac{n}{3}\rceil$ using $\lceil\frac{n}{3}\rceil$ qubits, and that an ensemble of such models can represent any function on the Boolean cube with degree $ d\leq \lceil\frac{n}{3}\rceil$. Furthermore, we discuss the potential benefits of each embedding and the impact of serial repetitions. Lastly, we demonstrate the use of the embeddings presented by performing numerical simulations and experiments on IBM quantum processors using the Qiskit machine learning framework.

2204.05286v3

Dealing with quantum computer readout noise through high energy physics unfolding methods

Quantum computers have the potential to solve problems that are intractable to classical computers, nevertheless they have high error rates. One significant kind of errors is known as Readout Errors. Current methods, as the matrix inversion and least-squares, are used to unfold (correct) readout errors. But these methods present many problems like oscillatory behavior and unphysical outcomes. In 2020 Benjamin Nachman et al. suggested a technique currently used in HEP, to correct detector effects. This method is known as the Iterative Bayesian Unfolding (IBU), and they have proven its effectiveness in mitigating readout errors, avoiding problems of the mentioned methods. Therefore, the main objective of our thesis is to mitigate readout noise of quantum computers, using this powerful unfolding method. For this purpose we generated a uniform distribution in the Yorktown IBM Q Machine, for 5 Qubits, in order to unfold it by IBU after being distorted by noise. Then we repeated the same experiment with a Gaussian distribution. Very satisfactory results and consistent with those of B. Nachman et al., were obtained. After that, we took a second purpose to explore unfolding in a larger qubit system, where we succeed to unfold a uniform distribution for 7 Qubits, distorted by noise from the Melbourne IBM Q Machine. In this case, the IBU method showed much better results than other techniques.

2204.05757v1

High-fidelity quantum control by polychromatic pulse trains

We introduce a quantum control technique using polychromatic pulse sequences (PPS), consisting of pulses with different carrier frequencies, i.e. different detunings with respect to the qubit transition frequency. We derive numerous PPS, which generate broadband, narrowband, and passband excitation profiles for different target transition probabilities. This makes it possible to create high-fidelity excitation profiles which are either (i) robust to deviations in the experimental parameters, which is attractive for quantum computing, or (ii) more sensitive to such variations, which is attractive for cross talk elimination and quantum sensing. The method is demonstrated experimentally using one of IBM's superconducting quantum processors, in a very good agreement between theory and experiment. These results demonstrate both the excellent coherence properties of the IBM qubits and the accuracy, robustness and flexibility of the proposed quantum control technique. They also show that the detuning is as efficient control parameter as the pulse phase that is commonly used in composite pulses. Hence the method opens a variety of perspectives for quantum control in areas where phase manipulation is difficult or inaccurate.

2204.02147v1

Performance of surface codes in realistic quantum hardware

Surface codes are generally studied based on the assumption that each of the qubits that make up the surface code lattice suffers noise that is independent and identically distributed (i.i.d.). However, real benchmarks of the individual relaxation ($T_1$) and dephasing ($T_2$) times of the constituent qubits of state-of-the-art quantum processors have recently shown that the decoherence effects suffered by each particular qubit actually vary in intensity. In consequence, in this article we introduce the independent non-identically distributed (i.ni.d.) noise model, a decoherence model that accounts for the non-uniform behaviour of the docoherence parameters of qubits. Additionally, we use the i.ni.d model to study how it affects the performance of a specific family of Quantum Error Correction (QEC) codes known as planar codes. For this purpose we employ data from four state-of-the-art superconducting processors: ibmq\_brooklyn, ibm\_washington, Zuchongzhi and Rigetti Aspen-M-1. Our results show that the i.i.d. noise assumption overestimates the performance of surface codes, which can suffer up to $95\%$ performance decrements in terms of the code pseudo-threshold when they are subjected to the i.ni.d. noise model. Furthermore, we consider and describe two methods which enhance the performance of planar codes under i.ni.d. noise. The first method involves a so-called re-weighting process of the conventional minimum weight perfect matching (MWPM) decoder, while the second one exploits the relationship that exists between code performance and qubit arrangement in the surface code lattice. The optimum qubit configuration derived through the combination of the previous two methods can yield planar code pseudo-threshold values that are up to $650\%$ higher than for the traditional MWPM decoder under i.ni.d. noise.

2203.15695v2

Measurement-based interleaved randomised benchmarking using IBM processors

Quantum computers have the potential to outperform classical computers in a range of computational tasks, such as prime factorisation and unstructured searching. However, real-world quantum computers are subject to noise. Quantifying noise is of vital importance, since it is often the dominant factor preventing the successful realisation of advanced quantum computations. Here we propose and demonstrate an interleaved randomised benchmarking protocol for measurement-based quantum computers that can be used to estimate the fidelity of any single-qubit measurement-based gate. We tested the protocol on IBM superconducting quantum processors by estimating the fidelity of the Hadamard and T gates - a universal single-qubit gate set. Measurements were performed on entangled cluster states of up to 31 qubits. Our estimated gate fidelities show good agreement with those calculated from quantum process tomography. By artificially increasing noise, we were able to show that our protocol detects large noise variations in different implementations of a gate.

2203.14995v2

Unentangled quantum reinforcement learning agents in the OpenAI Gym

Classical reinforcement learning (RL) has generated excellent results in different regions; however, its sample inefficiency remains a critical issue. In this paper, we provide concrete numerical evidence that the sample efficiency (the speed of convergence) of quantum RL could be better than that of classical RL, and for achieving comparable learning performance, quantum RL could use much (at least one order of magnitude) fewer trainable parameters than classical RL. Specifically, we employ the popular benchmarking environments of RL in the OpenAI Gym, and show that our quantum RL agent converges faster than classical fully-connected neural networks (FCNs) in the tasks of CartPole and Acrobot under the same optimization process. We also successfully train the first quantum RL agent that can complete the task of LunarLander in the OpenAI Gym. Our quantum RL agent only requires a single-qubit-based variational quantum circuit without entangling gates, followed by a classical neural network (NN) to post-process the measurement output. Finally, we could accomplish the aforementioned tasks on the real IBM quantum machines. To the best of our knowledge, none of the earlier quantum RL agents could do that.

2203.14348v1

Implementation of single-qubit measurement-based t-designs using IBM processors

Random unitary matrices sampled from the uniform Haar ensemble have a number of important applications both in cryptography and in the simulation of a variety of fundamental physical systems. Since the Haar ensemble is very expensive to sample, pseudorandom ensembles in the form of t-designs are frequently used as an efficient substitute, and are sufficient for most applications. We investigate t-designs generated using a measurement-based approach on superconducting quantum computers. In particular, we implemented an exact single-qubit 3-design on IBM quantum processors by performing measurements on a 6-qubit graph state. By analysing channel tomography results, we were able to show that the ensemble of unitaries realised was a 1-design, but not a 2-design or a 3-design under the test conditions set, which we show to be a result of depolarising noise during the measurement-based process. We obtained improved results for the 2-design test by implementing an approximate 2-design, in which measurements were performed on a smaller 5-qubit graph state, but the test still did not pass for all states. This suggests that the practical realisation of measurement-based t-designs on superconducting quantum computers will require further work on the reduction of depolarising noise in these devices.

2203.13092v1

Linear-depth quantum circuits for multiqubit controlled gates

Quantum circuit depth minimization is critical for practical applications of circuit-based quantum computation. In this work, we present a systematic procedure to decompose multiqubit controlled unitary gates, which is essential in many quantum algorithms, to controlled-NOT and single-qubit gates with which the quantum circuit depth only increases linearly with the number of control qubits. Our algorithm does not require any ancillary qubits and achieves a quadratic reduction of the circuit depth against known methods. We show the advantage of our algorithm with proof-of-principle experiments on the IBM quantum cloud platform.

2203.11882v2

Information loss and run time from practical application of quantum data compression

We examine information loss, resource costs, and run time from practical application of quantum data compression. Compressing quantum data to fewer qubits enables efficient use of resources, as well as applications for quantum communication and denoising. In this context, we provide a description of the quantum and classical components of the hybrid quantum autoencoder algorithm, implemented using IBM's Qiskit language. Utilizing our own data sets, we encode bitmap images as quantum superposition states, which correspond to linearly independent vectors with density matrices of discrete values. We successfully compress this data with near-lossless compression using simulation, and then run our algorithm on an IBMQ quantum chip. We describe conditions and run times for compressing our data on quantum devices.

2203.11332v1

Recursive Variational Quantum Compiling

Variational quantum compiling (VQC) algorithms aim to approximate deep quantum circuits with shallow parameterized ansatzes, making them more suitable for NISQ hardware. In this article a variant of VQC named the recursive variational quantum compiling (RVQC) algorithm is proposed. Existing VQC algorithms typically require coherently executing the full circuit during compilation. Under the influence of noise, sufficiently deep target circuits make compiling unfeasible using ordinary VQC. Since the compiling is often accomplished using a gradient-based quantum-classical approach, the quantum noise manifest as a noisy gradient during optimization, making convergence hard to obtain. On the other hand, RVQC can compile a circuit by first dividing it into $N$ shorter sub-circuits, then evaluate one sub-circuit at a time. As a result, the circuit depth required to implement RVQC is not dependent on the depth of the target circuit, but on the depth of the sub-circuits. Choosing a high enough $N$ thus ensures sufficiently shallow sub-circuit which can be successfully compiled individually. RVQC was compared with VQC on a noise model of the IBM Santiago device with the goal of compiling several randomly generated five-qubit circuits of approximately depth 1000. It was shown that VQC was not able to converge within 500 iterations of optimization. On the other hand, RVQC was able to converge to a fidelity of $0.90 \pm 0.05$ within a total of 500 iterations when splitting the target circuits into $N = 5$ parts.

2203.08514v2

Decoherence predictions in a superconductive quantum device using the steepest-entropy-ascent quantum thermodynamics framework

The current stage of quantum computing technology, called noisy intermediate-scale quantum (NISQ) technology, is characterized by large errors that prohibit it from being used for real applications. In these devices, decoherence, one of the main sources of error, is generally modeled by Markovian master equations such as the Lindblad master equation. In this work, the decoherence phenomena are addressed from the perspective of the steepest-entropy-ascent quantum thermodynamics (SEAQT) framework in which the noise is in part seen as internal to the system. The framework is as well used to describe changes in the energy associated with environmental interactions. Three scenarios, an inversion recovery experiment, a Ramsey experiment, and a two-qubit entanglement-disentanglement experiment, are used to demonstrate the applicability of this framework, which provides good results relative to the experiments and the Lindblad equation, It does so, however, from a different perspective as to the cause of the decoherence. These experiments are conducted on the IBM superconductive quantum device ibmq_bogota.

2203.08329v2

Ancilla-free implementation of generalized measurements for qubits embedded in a qudit space

Informationally complete (IC) positive operator-valued measures (POVMs) are generalized quantum measurements that offer advantages over the standard computational basis readout of qubits. For instance, IC-POVMs enable efficient extraction of operator expectation values, a crucial step in many quantum algorithms. POVM measurements are typically implemented by coupling one additional ancilla qubit to each logical qubit, thus imposing high demands on the device size and connectivity. Here, we show how to implement a general class of IC-POVMs without ancilla qubits. We exploit the higher-dimensional Hilbert space of a qudit in which qubits are often encoded. POVMs can then be realized by coupling each qubit to two of the available qudit states, followed by a projective measurement. We develop the required control pulse sequences and numerically establish their feasibility for superconducting transmon qubits through pulse-level simulations. Finally, we present an experimental demonstration of a qudit-space POVM measurement on IBM Quantum hardware. This paves the way to making POVM measurements broadly available to quantum computing applications.

2203.07369v1

QuFI: a Quantum Fault Injector to Measure the Reliability of Qubits and Quantum Circuits

Quantum computing is a new technology that is expected to revolutionize the computation paradigm in the next few years. Qubits exploit the quantum physics proprieties to increase the parallelism and speed of computation. Unfortunately, besides being intrinsically noisy, qubits have also been shown to be highly susceptible to external sources of faults, such as ionizing radiation. The latest discoveries highlight a much higher radiation sensitivity of qubits than traditional transistors and identify a much more complex fault model than bit-flip. We propose a framework to identify the quantum circuits sensitivity to radiation-induced faults and the probability for a fault in a qubit to propagate to the output. Based on the latest studies and radiation experiments performed on real quantum machines, we model the transient faults in a qubit as a phase shift with a parametrized magnitude. Additionally, our framework can inject multiple qubit faults, tuning the phase shift magnitude based on the proximity of the qubit to the particle strike location. As we show in the paper, the proposed fault injector is highly flexible, and it can be used on both quantum circuit simulators and real quantum machines. We report the finding of more than 285M injections on the Qiskit simulator and 53K injections on real IBM machines. We consider three quantum algorithms and identify the faults and qubits that are more likely to impact the output. We also consider the fault propagation dependence on the circuit scale, showing that the reliability profile for some quantum algorithms is scale-dependent, with increased impact from radiation-induced faults as we increase the number of qubits. Finally, we also consider multi qubits faults, showing that they are much more critical than single faults. The fault injector and the data presented in this paper are available in a public repository to allow further analysis.

2203.07183v1

Comparative study of adaptive variational quantum eigensolvers for multi-orbital impurity models

Hybrid quantum-classical embedding methods for correlated materials simulations provide a path towards potential quantum advantage. However, the required quantum resources arising from the multi-band nature of $d$ and $f$ electron materials remain largely unexplored. Here we compare the performance of different variational quantum eigensolvers in ground state preparation for interacting multi-orbital embedding impurity models, which is the computationally most demanding step in quantum embedding theories. Focusing on adaptive algorithms and models with 8 spin-orbitals, we show that state preparation with fidelities better than $99.9\%$ can be achieved using about $2^{14}$ shots per measurement circuit. When including gate noise, we observe that parameter optimizations can still be performed if the two-qubit gate error lies below $10^{-3}$, which is slightly smaller than current hardware levels. Finally, we measure the ground state energy on IBM and Quantinuum hardware using a converged adaptive ansatz and obtain a relative error of $0.7\%$.

2203.06745v3

Quantum Volume in Practice: What Users Can Expect from NISQ Devices

Quantum volume (QV) has become the de-facto standard benchmark to quantify the capability of Noisy Intermediate-Scale Quantum (NISQ) devices. While QV values are often reported by NISQ providers for their systems, we perform our own series of QV calculations on 24 NISQ devices currently offered by IBM Q, IonQ, Rigetti, Oxford Quantum Circuits, and Quantinuum (formerly Honeywell). Our approach characterizes the performances that an advanced user of these NISQ devices can expect to achieve with a reasonable amount of optimization, but without white-box access to the device. In particular, we compile QV circuits to standard gate sets of the vendor using compiler optimization routines where available, and we perform experiments across different qubit subsets. We find that running QV tests requires very significant compilation cycles, QV values achieved in our tests typically lag behind officially reported results and also depend significantly on the classical compilation effort invested.

2203.03816v5

Solving Nuclear Structure Problems with the Adaptive Variational Quantum Algorithm

We use the Lipkin-Meshkov-Glick (LMG) model and the valence-space nuclear shell model to examine the likely performance of variational quantum eigensolvers in nuclear-structure theory. The LMG model exhibits both a phase transition and spontaneous symmetry breaking at the mean-field level in one of the phases, features that characterize collective dynamics in medium-mass and heavy nuclei. We show that with appropriate modifications, the ADAPT-VQE algorithm, a particularly flexible and accurate variational approach, is not troubled by these complications. We treat up to 12 particles and show that the number of quantum operations needed to approach the ground-state energy scales linearly with the number of qubits. We find similar scaling when the algorithm is applied to the nuclear shell model with realistic interactions in the $sd$ and $pf$ shells. Although most of these simulations contain no noise, we use a noise model from real IBM hardware to show that for the LMG model with four particles, weak noise has no effect on the efficiency of the algorithm.

2203.01619v2

Simulating excited states of the Lipkin model on a quantum computer

We simulate the excited states of the Lipkin model using the recently proposed Quantum Equation of Motion (qEOM) method. The qEOM generalizes the EOM on classical computers and gives access to collective excitations based on quasi-boson operators $\hat{O}^\dagger_n(\alpha)$ of increasing configuration complexity $\alpha$. We show, in particular, that the accuracy strongly depends on the fermion to qubit encoding. Standard encoding leads to large errors, but the use of symmetries and the Gray code reduces the quantum resources and improves significantly the results on current noisy quantum devices. With this encoding scheme, we use IBM quantum machines to compute the energy spectrum for a system of $N=2, 3$ and $4$ particles and compare the accuracy against the exact solution. We found that the results of the approach with $\alpha = 2$, an analog of the second random phase approximation (SRPA), are, in principle, more accurate than with $\alpha = 1$, which corresponds to the random phase approximation (RPA), but the SRPA is more amenable to noise for large coupling strengths. Thus, the proposed scheme shows potential for achieving higher spectroscopic accuracy by implementations with higher configuration complexity, if a proper error mitigation method is applied.

2203.01478v3

Impact of quantum noise on the training of quantum Generative Adversarial Networks

Current noisy intermediate-scale quantum devices suffer from various sources of intrinsic quantum noise. Overcoming the effects of noise is a major challenge, for which different error mitigation and error correction techniques have been proposed. In this paper, we conduct a first study of the performance of quantum Generative Adversarial Networks (qGANs) in the presence of different types of quantum noise, focusing on a simplified use case in high-energy physics. In particular, we explore the effects of readout and two-qubit gate errors on the qGAN training process. Simulating a noisy quantum device classically with IBM's Qiskit framework, we examine the threshold of error rates up to which a reliable training is possible. In addition, we investigate the importance of various hyperparameters for the training process in the presence of different error rates, and we explore the impact of readout error mitigation on the results.

2203.01007v1

Summary: Chicago Quantum Exchange (CQE) Pulse-level Quantum Control Workshop

Quantum information processing holds great promise for pushing beyond the current frontiers in computing. Specifically, quantum computation promises to accelerate the solving of certain problems, and there are many opportunities for innovation based on applications in chemistry, engineering, and finance. To harness the full potential of quantum computing, however, we must not only place emphasis on manufacturing better qubits, advancing our algorithms, and developing quantum software. To scale devices to the fault tolerant regime, we must refine device-level quantum control. On May 17-18, 2021, the Chicago Quantum Exchange (CQE) partnered with IBM Quantum and Super.tech to host the Pulse-level Quantum Control Workshop. At the workshop, representatives from academia, national labs, and industry addressed the importance of fine-tuning quantum processing at the physical layer. The purpose of this report is to summarize the topics of this meeting for the quantum community at large.

2202.13600v1

Simulating spectroscopy experiments with a superconducting quantum computer

We present a novel method for solving eigenvalue problems on a quantum computer based on spectroscopy. The method works by coupling a "probe" qubit to a set of system simulation qubits and then time evolving both the probe and the system under Hamiltonian dynamics. In this way, we simulate spectroscopy on a quantum computer. We test our method on the IBM quantum hardware for a simple single spin model and an interacting Kitaev chain model. For the Kitaev chain, we trace out the pseudo-topological phase boundary for a two-site model.

2202.12910v3

Quantum Error Correction Scheme for Fully Correlated Noise

This paper investigates quantum error correction schemes for fully-correlated noise channels on an $n$-qubit system, where error operators take the form $W^{\otimes n}$, with $W$ being an arbitrary $2\times 2$ unitary operator. In previous literature, a recursive quantum error correction scheme can be used to protect $k$ qubits using $(k+1)$-qubit ancilla. We implement this scheme on 3-qubit and 5-qubit channels using the IBM quantum computers, where we uncover an error in the previous paper related to the decomposition of the encoding/decoding operator into elementary quantum gates. Here, we present a modified encoding/decoding operator that can be efficiently decomposed into (a) standard gates available in the \texttt{qiskit} library and (b) basic gates comprised of single-qubit gates and CNOT gates. Since IBM quantum computers perform relatively better with fewer basic gates, a more efficient decomposition gives more accurate results. Our experiments highlight the importance of an efficient decomposition for the encoding/decoding operators and demonstrate the effectiveness of our proposed schemes in correcting quantum errors. Furthermore, we explore a special type of channel with error operators of the form $\sigma_x^{\otimes n}, \sigma_y^{\otimes n}$ and $\sigma_z^{\otimes n}$, where $\sigma_x, \sigma_y, \sigma_z$ are the Pauli matrices. For these channels, we implement a hybrid quantum error correction scheme that protects both quantum and classical information using IBM's quantum computers. We conduct experiments for $n = 3, 4, 5$ and show significant improvements compared to recent work.

2202.12408v2

Improved variational quantum eigensolver via quasi-dynamical evolution

The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed for current and near-term quantum devices. Despite its initial success, there is a lack of understanding involving several of its key aspects. There are problems with VQE that forbid a favourable scaling towards quantum advantage. In order to alleviate the problems, we propose and extensively test a quantum annealing inspired heuristic that supplements VQE. The improved VQE enables an efficient initial state preparation mechanism, in a recursive manner, for a quasi-dynamical unitary evolution. We conduct an in-depth scaling analysis of finding the ground state energies with increasing lattice sizes of the Heisenberg model, employing simulations of up to $40$ qubits that manipulate the complete state vector. For the current devices, we further propose a benchmarking toolkit using a mean-field model and test it on IBM Q devices. The improved VQE avoids barren plateaus, exits local minima, and works with low-depth circuits. Realistic gate execution times estimate a longer computational time to complete the same computation on a fully functional error-free quantum computer than on a quantum computer emulator implemented on a classical computer. However, our proposal can be expected to help accurate estimations of the ground state energies beyond $50$ qubits when the complete state vector can no longer be stored on a classical computer, thus enabling quantum advantage.

2202.10130v3

Experimental demonstration of composite pulses on IBM's quantum computer

We perform comprehensive experimental tests of various composite pulse sequences using one of open-access IBM's quantum processors, based on superconducting transmon qubits. We implement explicit pulse control of the qubit by making use of the opportunity of low-level access to the backend, provided by IBM Quantum. We obtain the excitation profiles for a huge variety of broadband, narrowband, and passband composite pulses, producing any pre-chosen target probabilities, ranging from zero to one. We also test universal composite pulses which compensate errors in any experimental parameter. In all experiments, we find excellent agreement between theoretical and experimental excitation profiles. This proves both the composite pulses as a very efficient and flexible quantum control tool and the high quality of the IBM quantum processor. As an extreme example, we test and observe a pronounced narrowband excitation profile for a composite sequence of as many as 1001 pulses.

2202.09647v1

Holonomic control of a three-qubits system in an NV center using a near-term quantum computer

The holonomic approach to controlling (nitrogen-vacancy) NV-center qubits provides an elegant way of theoretically devising universal quantum gates that operate on qubits via calculable microwave pulses. There is, however, a lack of simulated results from the theory of holonomic control of quantum registers with more than two qubits describing the transition between the dark states. In light of this, we have been experimenting with the IBM Quantum Experience technology to determine the capabilities of simulating holonomic control of NV-centers for three qubits describing an eight-level system that produces a non-Abelian geometric phase. The tunability of the geometric phase via the detuning frequency is demonstrated through the high fidelity (about 80%) of 3-qubit off-resonant holonomic gates over the on-resonant ones. The transition between the dark states shows the alignment of the gate dark state with the qubits initial state hence decoherence of the multi-qubit system is well-controlled through a 0.33pi rotation. The electron return probability can exhibit spin-orbit coupling-like behavior as observed in topological materials based on the extra geometric phase.

2202.08061v1

Quantifying information scrambling via Classical Shadow Tomography on Programmable Quantum Simulators

We develop techniques to probe the dynamics of quantum information, and implement them experimentally on an IBM superconducting quantum processor. Our protocols adapt shadow tomography for the study of time evolution channels rather than of quantum states, and rely only on single-qubit operations and measurements. We identify two unambiguous signatures of quantum information scrambling, neither of which can be mimicked by dissipative processes, and relate these to many-body teleportation. By realizing quantum chaotic dynamics in experiment, we measure both signatures, and support our results with numerical simulations of the quantum system. We additionally investigate operator growth under this dynamics, and observe behaviour characteristic of quantum chaos. As our methods require only a single quantum state at a time, they can be readily applied on a wide variety of quantum simulators.

2202.05132v2

Markovian Noise Modelling and Parameter Extraction Framework for Quantum Devices

In recent years, Noisy Intermediate Scale Quantum (NISQ) computers have been widely used as a test bed for quantum dynamics. This work provides a new hardware-agnostic framework for modelling the Markovian noise and dynamics of quantum systems in benchmark procedures used to evaluate device performance. As an accessible example, the application and performance of this framework is demonstrated on IBM Quantum computers. This framework serves to extract multiple calibration parameters simultaneously through a simplified process which is more reliable than previously studied calibration experiments and tomographic procedures. Additionally, this method allows for real-time calibration of several hardware parameters of a quantum computer within a comprehensive procedure, providing quantitative insight into the performance of each device to be accounted for in future quantum circuits. The framework proposed here has the additional benefit of highlighting the consistency among qubit pairs when extracting parameters, which leads to a less computationally expensive calibration process than evaluating the entire device at once.

2202.04474v3

Methods and Results for Quantum Optimal Pulse Control on Superconducting Qubit Systems

The effective use of current Noisy Intermediate-Scale Quantum (NISQ) devices is often limited by the noise which is caused by interaction with the environment and affects the fidelity of quantum gates. In transmon qubit systems, the quantum gate fidelity can be improved by applying control pulses that can minimize the effects of the environmental noise. In this work, we employ physics-guided quantum optimal control strategies to design optimal pulses driving quantum gates on superconducting qubit systems. We test our results by conducting experiments on the IBM Q hardware using their OpenPulse API. We compare the performance of our pulse-optimized quantum gates against the default quantum gates and show that the optimized pulses improve the fidelity of the quantum gates, in particular the single-qubit gates. We discuss the challenges we encountered in our work and point to possible future improvements.

2202.03260v2

Parallel Quantum Chemistry on Noisy Intermediate-Scale Quantum Computers

A novel parallel hybrid quantum-classical algorithm for the solution of the quantum-chemical ground-state energy problem on gate-based quantum computers is presented. This approach is based on the reduced density-matrix functional theory (RDMFT) formulation of the electronic structure problem. For that purpose, the density-matrix functional of the full system is decomposed into an indirectly coupled sum of density-matrix functionals for all its subsystems using the adaptive cluster approximation to RDMFT. The approximations involved in the decomposition and the adaptive cluster approximation itself can be systematically converged to the exact result. The solutions for the density-matrix functionals of the effective subsystems involves a constrained minimization over many-particle states that are approximated by parametrized trial states on the quantum computer similarly to the variational quantum eigensolver. The independence of the density-matrix functionals of the effective subsystems introduces a new level of parallelization and allows for the computational treatment of much larger molecules on a quantum computer with a given qubit count. In addition, for the proposed algorithm techniques are presented to reduce the qubit count, the number of quantum programs, as well as its depth. The new approach is demonstrated for Hubbard-like systems on IBM quantum computers based on superconducting transmon qubits.

2202.02417v2

Learning entanglement breakdown as a phase transition by confusion

Quantum technologies require methods for preparing and manipulating entangled multiparticle states. However, the problem of determining whether a given quantum state is entangled or separable is known to be an NP-hard problem in general, and even the task of detecting entanglement breakdown for a given class of quantum states is difficult. In this work, we develop an approach for revealing entanglement breakdown using a machine learning technique, which is known as 'learning by confusion'. We consider a family of quantum states, which is parameterized such that there is a single critical value dividing states within this family into separate and entangled. We demonstrate the 'learning by confusion' scheme allows us to determine the critical value. Specifically, we study the performance of the method for the two-qubit, two-qutrit, and two-ququart entangled state. In addition, we investigate the properties of the local depolarization and the generalized amplitude damping channel in the framework of the confusion scheme. Within our approach and setting the parameterization of special trajectories, we obtain an entanglement-breakdown 'phase diagram' of a quantum channel, which indicates regions of entangled (separable) states and the entanglement-breakdown region. Then we extend the way of using the 'learning by confusion' scheme for recognizing whether an arbitrary given state is entangled or separable. We show that the developed method provides correct answers for a variety of states, including entangled states with positive partial transpose. We also present a more practical version of the method, which is suitable for studying entanglement breakdown in noisy intermediate-scale quantum devices. We demonstrate its performance using an available cloud-based IBM quantum processor.

2202.00348v3

Quantum simulation of dissipative collective effects on noisy quantum computers

Dissipative collective effects are ubiquitous in quantum physics, and their relevance ranges from the study of entanglement in biological systems to noise mitigation in quantum computers. Here, we put forward the first fully quantum simulation of dissipative collective phenomena on a real quantum computer, based on the recently introduced multipartite collision model. First, we theoretically study the accuracy of this algorithm on near-term quantum computers with noisy gates, and we derive some rigorous error bounds that depend on the timestep of the collision model and on the gate errors. These bounds can be employed to estimate the necessary resources for the efficient quantum simulation of the collective dynamics. Then, we implement the algorithm on some IBM quantum computers to simulate superradiance and subradiance between a pair of qubits. Our experimental results successfully display the emergence of collective effects in the quantum simulation. In addition, we analyze the noise properties of the gates that we employ in the algorithm by means of full process tomography, with the aim of improving our understanding of the errors in the near-term devices that are currently accessible to worldwide researchers. We obtain the values of the average gate fidelity, unitarity, incoherence and diamond error, and we establish a connection between them and the accuracy of the experimentally simulated state. Moreover, we build a noise model based on the results of the process tomography for two-qubit gates and show that its performance is comparable with the noise model provided by IBM. Finally, we observe that the scaling of the error as a function of the number of gates is favorable, but at the same time reaching the threshold of the diamond errors for quantum fault tolerant computation may still be orders of magnitude away in the devices that we employ.

2201.11597v2

An Efficient Quantum Readout Error Mitigation for Sparse Measurement Outcomes of Near-term Quantum Devices

The readout error on the near-term quantum devices is one of the dominant noise factors, which can be mitigated by classical post-processing called quantum readout error mitigation (QREM). The standard QREM method applies the inverse of noise calibration matrix to the outcome probability distribution using exponential computational resources to the system size. Hence this standard approach is not applicable to the current quantum devices with tens of qubits and more. We propose two efficient QREM methods on such devices whose computational complexity is $O(ns^2)$ for probability distributions on measuring $n$ qubits with $s$ shots. The main targets of the proposed methods are the sparse probability distributions where only a few states are dominant. We compare the proposed methods with several recent QREM methods on the following three cases: expectation values of GHZ state, its fidelities, and the estimation error of maximum likelihood amplitude estimation (MLAE) algorithm with modified Grover iterator. The two cases of the GHZ state are on real IBM quantum devices, while the third is by numerical simulation. The proposed methods can be applied to mitigate GHZ states up to 65 qubits on IBM Quantum devices within a few seconds to confirm the existence of a 29-qubit GHZ state with fidelity larger than 0.5. The proposed methods also succeed in the estimation of the amplitude in MLAE with the modified Grover operator where other QREM methods fail.

2201.11046v2

Implementation of quantum compression on IBM quantum computers

Advances in development of quantum computing processors brought ample opportunities to test the performance of various quantum algorithms with practical implementations. In this paper we report on implementations of quantum compression algorithm that can efficiently compress unknown quantum information. We restricted ourselves to compression of three pure qubits into two qubits, as the complexity of even such a simple implementation is barely within the reach of today's quantum processors. We implemented the algorithm on IBM quantum processors with two different topological layouts - a fully connected triangle processor and a partially connected line processor. It turns out that the incomplete connectivity of the line processor affects the performance only minimally. On the other hand, it turns out that the transpilation, i.e. compilation of the circuit into gates physically available to the quantum processor, crucially influences the result. We also have seen that the compression followed by immediate decompression is, even for such a simple case, on the edge or even beyond the capabilities of currently available quantum processors.

2201.10999v1

Experimental realization of quantum teleportation of arbitrary single and two-qubit states via hypergraph states

Here we demonstrate quantum teleportation through hypergraph states, which are the generalization of graph states, and due to their non-local entanglement properties, it allows us to perform quantum teleportation. Here we design some hypergraph states useful for quantum teleportation and process the schemes for quantum teleportation of single-qubit and two-qubit arbitrary states via three-uniform three-qubit and four-qubit hypergraph states respectively. We explicate the experimental realization of quantum teleportation of both single and two-qubit arbitrary states. Then we run our quantum circuits on the IBM quantum experience platform, where we present the results obtained by both the simulator and real devices such as "ibmq_qasm_simulator" and "ibmq_16_melbourne" and calculate the fidelity. We observe that the real device has some errors in comparison to the simulator, these errors are due to the decoherence effect in the quantum channel and gate errors. We then illustrate the experimental and theoretical density matrices of teleported single and two-qubit states.

2201.08234v1

Quantum Memristors with Quantum Computers

We propose the encoding of memristive quantum dynamics on a digital quantum computer. Using a set of auxiliary qubits, we simulate an effective non-Markovian environment inspired by a collisional model, reproducing memristive features between expectation values of different operators in a single qubit. We numerically test our proposal in an IBM quantum simulator with 32 qubits, obtaining the pinched hysteresis curve that is characteristic of a quantum memristor. Furthermore, we extend our method to the case of two coupled quantum memristors, opening the door to the study of neuromorphic quantum computing in the NISQ era.

2112.14660v1

Active Learning of Quantum System Hamiltonians yields Query Advantage

Hamiltonian learning is an important procedure in quantum system identification, calibration, and successful operation of quantum computers. Through queries to the quantum system, this procedure seeks to obtain the parameters of a given Hamiltonian model and description of noise sources. Standard techniques for Hamiltonian learning require careful design of queries and $O(\epsilon^{-2})$ queries in achieving learning error $\epsilon$ due to the standard quantum limit. With the goal of efficiently and accurately estimating the Hamiltonian parameters within learning error $\epsilon$ through minimal queries, we introduce an active learner that is given an initial set of training examples and the ability to interactively query the quantum system to generate new training data. We formally specify and experimentally assess the performance of this Hamiltonian active learning (HAL) algorithm for learning the six parameters of a two-qubit cross-resonance Hamiltonian on four different superconducting IBM Quantum devices. Compared with standard techniques for the same problem and a specified learning error, HAL achieves up to a $99.8\%$ reduction in queries required, and a $99.1\%$ reduction over the comparable non-adaptive learning algorithm. Moreover, with access to prior information on a subset of Hamiltonian parameters and given the ability to select queries with linearly (or exponentially) longer system interaction times during learning, HAL can exceed the standard quantum limit and achieve Heisenberg (or super-Heisenberg) limited convergence rates during learning.

2112.14553v1

A Divide-and-Conquer Approach to Dicke State Preparation

We present a divide-and-conquer approach to deterministically prepare Dicke states $\lvert D_k^n\rangle$ (i.e., equal-weight superpositions of all $n$-qubit states with Hamming Weight $k$) on quantum computers. In an experimental evaluation for up to $n=6$ qubits on IBM Quantum Sydney and Montreal devices, we achieve significantly higher state fidelity compared to previous results [Mukherjee and others, TQE'2020], [Cruz and others, QuTe'2019]. The fidelity gains are achieved through several techniques: Our circuits first "divide" the Hamming weight between blocks of $n/2$ qubits, and then "conquer" those blocks with improved versions of Dicke state unitaries [B\"artschi and others, FCT'2019]. Due to the sparse connectivity on IBM's heavy-hex-architectures, these circuits are implemented for linear nearest neighbor topologies. Further gains in (estimating) the state fidelity are due to our use of measurement error mitigation and hardware progress.

2112.12435v2

Method for Generating Randomly Perturbed Density Operators Subject to Different Sets of Constraints

This paper presents a general method for producing randomly perturbed density operators subject to different sets of constraints. The perturbed density operators are a specified "distance" away from the state described by the original density operator. This approach is applied to a bipartite system of qubits and used to examine the sensitivity of various entanglement measures on the perturbation magnitude. The constraint sets used include constant energy, constant entropy, and both constant energy and entropy. The method is then applied to produce perturbed random quantum states that correspond with those obtained experimentally for Bell states on the IBM quantum device ibmq_manila. The results show that the methodology can be used to simulate the outcome of real quantum devices where noise, which is important both in theory and simulation, is present.

2112.12247v2

Practical Quantum State Tomography for Gibbs states

Quantum state tomography is an essential tool for the characterization and verification of quantum states. However, as it cannot be directly applied to systems with more than a few qubits, efficient tomography of larger states on mid-sized quantum devices remains an important challenge in quantum computing. We develop a tomography approach that requires moderate computational and quantum resources for the tomography of states that can be approximated by Gibbs states of local Hamiltonians. The proposed method, Hamiltonian Learning Tomography, uses a Hamiltonian learning algorithm to get a parametrized ansatz for the Gibbs Hamiltonian, and optimizes it with respect to the results of local measurements. We demonstrate the utility of this method with a high fidelity reconstruction of the density matrix of 4 to 10 qubits in a Gibbs state of the transverse-field Ising model, in numerical simulations as well as in experiments on IBM Quantum superconducting devices accessed via the cloud. Code implementation of the our method is freely available as an open source software in Python.

2112.10418v2

Protection of noisy multipartite entangled states of superconducting qubits via universally robust dynamical decoupling schemes

We demonstrate the efficacy of the universally robust dynamical decoupling (URDD) sequence to preserve multipartite maximally entangled quantum states on a cloud based quantum computer via the IBM platform. URDD is a technique that can compensate for experimental errors and simultaneously protect the state against environmental noise. To further improve the performance of the URDD sequence, phase randomization (PR) as well as correlated phase randomization (CPR) techniques are added to the basic URDD sequence. The performance of the URDD sequence is quantified by measuring the entanglement in several noisy entangled states (two-qubit triplet state, three-qubit GHZ state, four-qubit GHZ state and four-qubit cluster state) at several time points. Our experimental results demonstrate that the URDD sequence is successfully able to protect noisy multipartite entangled states and its performance is substantially improved by adding the phase randomization and correlated phase randomization sequences.

2112.10417v1

Performance Evaluations of Noisy Approximate Quantum Fourier Arithmetic

The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage and circuit approximation, for performing arithmetic operations on quantum computers, and offers a potential route towards a numerical quantum-computational paradigm. In this paper, we utilize efficient techniques to implement QFT-based integer addition and multiplications. These operations are fundamental to various quantum applications including Shor's algorithm, weighted sum optimization problems in data processing and machine learning, and quantum algorithms requiring inner products. We carry out performance evaluations of these implementations based on IBM's superconducting qubit architecture using different compatible noise models. We isolate the sensitivity of the component quantum circuits on both one-/two-qubit gate error rates, and the number of the arithmetic operands' superposed integer states. We analyze performance, and identify the most effective approximation depths for quantum add and quantum multiply within the given context. We observe significant dependency of the optimal approximation depth on the degree of machine noise and the number of superposed states in certain performance regimes. Finally, we elaborate on the algorithmic challenges - relevant to signed, unsigned, modular and non-modular versions - that could also be applied to current implementations of QFT-based subtraction, division, exponentiation, and their potential tensor extensions. We analyze performance trends in our results and speculate on possible future development within this computational paradigm.

2112.09349v1

Testing accuracy of qubit rotations on a public quantum computer

We analyze the results of the test of $\pi/2$ qubit rotations on the public quantum computer provided by IBM. We measure a single qubit rotated by $\pi/2$ about a random axis, and we accumulate vast statistics of the results. The test performed on different devices shows systematic deviations from the theoretical predictions, which appear at the level $10^{-3}$. Some of the differences, beyond 5 standard deviations, cannot be explained by simple corrections due to nonlinearities of pulse generations. The magnitude of the deviation is comparable with the randomized benchmarking of the gate, but we additionally observe a pronounced parametric dependence. We discuss other possible reasons of the deviations, including states beyond the single-qubit space. The deviations have a similar structure for various devices used at different times, and so they can also serve as a diagnostic tool to eliminate imperfect gate implementations, and faithful description of the involved physical systems.

2112.07567v4

A Case For Noisy Shallow Gate-Based Circuits In Quantum Machine Learning

There is increasing interest in the development of gate-based quantum circuits for the training of machine learning models. Yet, little is understood concerning the parameters of circuit design, and the effects of noise and other measurement errors on the performance of quantum machine learning models. In this paper, we explore the practical implications of key circuit design parameters (number of qubits, depth etc.) using several standard machine learning datasets and IBM's Qiskit simulator. In total we evaluate over 6500 unique circuits with $n \approx 120700$ individual runs. We find that in general shallow (low depth) wide (more qubits) circuit topologies tend to outperform deeper ones in settings without noise. We also explore the implications and effects of different notions of noise and discuss circuit topologies that are more / less robust to noise for classification machine learning tasks. Based on the findings we define guidelines for circuit topologies that show near-term promise for the realisation of quantum machine learning algorithms using gate-based NISQ quantum computer.

2112.06712v1

Learning Classical Readout Quantum PUFs based on single-qubit gates

Physical Unclonable Functions (PUFs) have been proposed as a way to identify and authenticate electronic devices. Recently, several ideas have been presented that aim to achieve the same for quantum devices. Some of these constructions apply single-qubit gates in order to provide a secure fingerprint of the quantum device. In this work, we formalize the class of Classical Readout Quantum PUFs (CR-QPUFs) using the statistical query (SQ) model and explicitly show insufficient security for CR-QPUFs based on single qubit rotation gates, when the adversary has SQ access to the CR-QPUF. We demonstrate how a malicious party can learn the CR-QPUF characteristics and forge the signature of a quantum device through a modelling attack using a simple regression of low-degree polynomials. The proposed modelling attack was successfully implemented in a real-world scenario on real IBM Q quantum machines. We thoroughly discuss the prospects and problems of CR-QPUFs where quantum device imperfections are used as a secure fingerprint.

2112.06661v2

Process Tomography on a 7-Qubit Quantum Processor via Tensor Network Contraction Path Finding

Quantum process tomography (QPT), where a quantum channel is reconstructed through the analysis of repeated quantum measurements, is an important tool for validating the operation of a quantum processor. We detail the combined use of an existing QPT approach based on tensor networks (TNs) and unsupervised learning with TN contraction path finding algorithms in order to use TNs of arbitrary topologies for reconstruction. Experiments were conducted on the 7-qubit IBM Quantum Falcon Processor ibmq_casablanca, where we demonstrate this technique by matching the topology of the tensor networks used for reconstruction with the topology of the processor, allowing us to extend past the characterisation of linear nearest neighbour circuits. Furthermore, we conduct single-qubit gate set tomography (GST) on each individual qubit to correct for separable errors during the state preparation and measurement phases of QPT, which are separate from the channel under consideration but may negatively impact the quality of its reconstruction. We are able to report a fidelity of 0.89 against the ideal unitary channel of a single-cycle random quantum circuit performed on ibmq_casablanca, after obtaining just $1.1 \times 10^5$ measurements for the reconstruction of this 7-qubit process. This represents more than five orders of magnitude fewer total measurements than the number needed to conduct full, traditional QPT on a 7-qubit process.

2112.06364v1

A Structured Method for Compilation of QAOA Circuits in Quantum Computing

Quantum Approximation Optimization Algorithm (QAOA) is a highly advocated variational algorithm for solving the combinatorial optimization problem. One critical feature in the quantum circuit of QAOA algorithm is that it consists of two-qubit operators that commute. The flexibility in reordering the two-qubit gates allows compiler optimizations to generate circuits with better depths, gate count, and fidelity. However, it also imposes significant challenges due to additional freedom exposed in the compilation. Prior studies lack the following: (1) Performance guarantee, (2) Scalability, and (3) Awareness of regularity in scalable hardware. We propose a structured method that ensures linear depth for any compiled QAOA circuit on multi-dimensional quantum architectures. We also demonstrate how our method runs on Google Sycamore and IBM Non-linear architectures in a scalable manner and in linear time. Overall, we can compile a circuit with up to 1024 qubits in 10 seconds with a 3.8X speedup in depth, 17% reduction in gate count, and 18X improvement for circuit ESP.

2112.06143v4

VAQEM: A Variational Approach to Quantum Error Mitigation

Variational Quantum Algorithms (VQAs) are relatively robust to noise, but errors are still a significant detriment to VQAs on near-term quantum machines. It is imperative to employ error mitigation techniques to improve VQA fidelity. While existing error mitigation techniques built from theory provide substantial gains, the disconnect between theory and real machine execution limits their benefits. Thus, it is critical to optimize mitigation techniques to explicitly suit the target application as well as the noise characteristics of the target machine. We propose VAQEM, which dynamically tailors existing error mitigation techniques to the actual, dynamic noisy execution characteristics of VQAs on a target quantum machine. We do so by tuning specific features of these mitigation techniques similar to the traditional rotation angle parameters - by targeting improvements towards a specific objective function which represents the VQA problem at hand. In this paper, we target two types of error mitigation techniques which are suited to idle times in quantum circuits: single qubit gate scheduling and the insertion of dynamical decoupling sequences. We gain substantial improvements to VQA objective measurements - a mean of over 3x across a variety of VQA applications, run on IBM Quantum machines. More importantly, the proposed variational approach is general and can be extended to many other error mitigation techniques whose specific configurations are hard to select a priori. Integrating more mitigation techniques into the VAQEM framework can lead to potentially realizing practically useful VQA benefits on today's noisy quantum machines.

2112.05821v1

General quantum Chinos games

The Chinos game is a non-cooperative game between players who try to guess the total sum of coins drawn collectively. Semiclassical and quantum versions of this game were proposed by F. Guinea and M. A. Martin-Delgado, in J. Phys. A: Math. Gen. 36 L197 (2003), where the coins are replaced by a boson whose number occupancy is the aim of player's guesses. Here, we propose other versions of the Chinos game using a hard-core boson, one qubit and two qubits. In the latter case, we find that using entangled states the second player has a stable winning strategy that becomes symmetric for non-entangled states. Finally, we use the IBM Quantum Experience to compute the basic quantities involved in the two-qubit version of the game

2112.05175v2

Quantum readout error mitigation via deep learning

Quantum computing devices are inevitably subject to errors. To leverage quantum technologies for computational benefits in practical applications, quantum algorithms and protocols must be implemented reliably under noise and imperfections. Since noise and imperfections limit the size of quantum circuits that can be realized on a quantum device, developing quantum error mitigation techniques that do not require extra qubits and gates is of critical importance. In this work, we present a deep learning-based protocol for reducing readout errors on quantum hardware. Our technique is based on training an artificial neural network with the measurement results obtained from experiments with simple quantum circuits consisting of singe-qubit gates only. With the neural network and deep learning, non-linear noise can be corrected, which is not possible with the existing linear inversion methods. The advantage of our method against the existing methods is demonstrated through quantum readout error mitigation experiments performed on IBM five-qubit quantum devices.

2112.03585v1

Comment on "Multi-output quantum teleportation of different quantum information with an IBM quantum experience"

Recently, Yu et al., (Commun. Theor. Phys. 73 (2021) 085103) has proposed a scheme for "multi-output quantum teleportation" and has implemented the scheme using an IBM quantum computer. In their so called multicast-based quantum teleportation scheme, a sender (Alice) teleported two different quantum states (one of which is a m-qubit GHZ class state and the other is a (m+1)-qubit GHZ class state) to the two receivers. To perform the task, a five-qubit cluster state was used as a quantum channel, and the scheme was realized using IBM quantum computer for m = 1. In this comment, it is shown that the quantum resources used by Yu et al., was extensively high. One can perform the same task of two-party quantum teleportation using two Bell states only. The modified scheme for multi-output teleportation using optimal resources has also been implemented using IBM quantum computer for m = 1 and the obtained result is compared with the result of Yu et al.

2112.03503v1

Hidden variables in Mermin GHZ machine with quantum assistance

Three experiments, with an IBM superconducting quantum computer, are presented, where the setting combinations on a three qubit GHZ(like) state were selected by two additional assistant qubits. The average of the polynomial of Mermin for the three entangled qubits was calculated; the results showed violation of the inequality of Mermin. However, given that the assistant qubits selected, imposed and informed the type of settings, it was possible to interpret the results in terms of arranged relations among hidden variables of the assistants and the entanglement BEFORE each shot; the hidden variables may or may not be local depending on the way the qubits were initialized.

2112.03689v1

A Quantum Approach to the Discretizable Molecular Distance Geometry Problem

The Discretizable Molecular Distance Geometry Problem (DMDGP) aims to determine the three-dimensional protein structure using distance information from nuclear magnetic resonance experiments. The DMDGP has a finite number of candidate solutions and can be solved by combinatorial methods. We describe a quantum approach to the DMDGP by using Grover's algorithm with an appropriate oracle function, which is more efficient than classical methods that use brute force. We show computational results by implementing our scheme on IBM quantum computers with a small number of noisy qubits.

2112.01303v1

Modelling quantum photonics on a quantum computer

Modelling of photonic devices traditionally involves solving the equations of light-matter interaction and light propagation, and it is restrained by their applicability. Here we demonstrate an alternative modelling methodology by creating a "quantum copy" of the optical device in the quantum computer. As an illustration, we simulate quantum interference of light on a thin absorbing film. Such interference can lead to either perfect absorption or total transmission of light through the film, the phenomena attracting attention for data processing applications in classical and quantum information networks. We map behaviour of the photon in the quantum interference experiment to the evolution of a quantum state of transmon, a superconducting charge qubit of the IBM quantum computer. Details of the real optical experiment are flawlessly reproduced on the quantum computer. We argue that superiority of the "quantum copy" methodology shall be apparent in modelling complex multi-photon optical phenomena and devices.

2111.15183v1

Quantum simulation of molecules in solution

Quantum chemical calculations on quantum computers have been focused mostly on simulating molecules in gas-phase. Molecules in liquid solution are however most relevant for Chemistry. Continuum solvation models represent a good compromise between computational affordability and accuracy in describing solvation effects within a quantum chemical description of solute molecules. Here we report on the extension of the Variational Quantum Eigensolver to solvated systems, using the Polarizable Continuum Model. We show that accounting for solvation effects does not impact the algorithmic efficiency. Numerical results of noiseless simulations for molecular systems with up to twelve spin-orbitals (qubits) are presented. Furthermore, calculations performed on a simulated quantum hardware (IBM Q Mumbai), thus including noise, yield computed solvation free energies in fair agreement with the classical calculations without the inclusion of any error mitigation protocol.

2111.13458v2

QuantumCircuitOpt: An Open-source Framework for Provably Optimal Quantum Circuit Design

In recent years, the quantum computing community has seen an explosion of novel methods to implement non-trivial quantum computations on near-term hardware. An important direction of research has been to decompose an arbitrary entangled state, represented as a unitary, into a quantum circuit, that is, a sequence of gates supported by a quantum processor. It has been well known that circuits with longer decompositions and more entangling multi-qubit gates are error-prone for the current noisy, intermediate-scale quantum devices. To this end, there has been a significant interest to develop heuristic-based methods to discover compact circuits. We contribute to this effort by proposing QuantumCircuitOpt (QCOpt), a novel open-source framework which implements mathematical optimization formulations and algorithms for decomposing arbitrary unitary gates into a sequence of hardware-native gates. A core innovation of QCOpt is that it provides optimality guarantees on the quantum circuits that it produces. In particular, we show that QCOpt can find up to 57% reduction in the number of necessary gates on circuits with up to four qubits, and in run times less than a few minutes on commodity computing hardware. We also validate the efficacy of QCOpt as a tool for quantum circuit design in comparison with a naive brute-force enumeration algorithm. We also show how the QCOpt package can be adapted to various built-in types of native gate sets, based on different hardware platforms like those produced by IBM, Rigetti and Google. We hope this package will facilitate further algorithmic exploration for quantum processor designers, as well as quantum physicists.

2111.11674v1

Quanto: Optimizing Quantum Circuits with Automatic Generation of Circuit Identities

Existing quantum compilers focus on mapping a logical quantum circuit to a quantum device and its native quantum gates. Only simple circuit identities are used to optimize the quantum circuit during the compilation process. This approach misses more complex circuit identities, which could be used to optimize the quantum circuit further. We propose Quanto, the first quantum optimizer that automatically generates circuit identities. Quanto takes as input a gate set and generates provably correct circuit identities for the gate set. Quanto's automatic generation of circuit identities includes single-qubit and two-qubit gates, which leads to a new database of circuit identities, some of which are novel to the best of our knowledge. In addition to the generation of new circuit identities, Quanto's optimizer applies such circuit identities to quantum circuits and finds optimized quantum circuits that have not been discovered by other quantum compilers, including IBM Qiskit and Cambridge Quantum Computing Tket. Quanto's database of circuit identities could be applied to improve existing quantum compilers and Quanto can be used to generate identity databases for new gate sets.

2111.11387v1

Exploring Airline Gate-Scheduling Optimization Using Quantum Computers

This paper investigates the application of quantum computing technology to airline gate-scheduling quadratic assignment problems (QAP). We explore the quantum computing hardware architecture and software environment required for porting classical versions of these type of problems to quantum computers. We discuss the variational quantum eigensolver and the inclusion of space-efficient graph coloring to the Quadratic Unconstrained Binary Optimization (QUBO). These enhanced quantum computing algorithms are tested with an 8 gate and 24 flight test case using both the IBM quantum computing simulator and a 27 qubit superconducting transmon IBM quantum computing hardware platform.

2111.09472v1

A Variation-Aware Quantum Circuit Mapping Approach Based on Multi-agent Cooperation

The quantum circuit mapping approach is an indispensable part of the software stack for the noisy intermediatescale quantum (NISQ) device. It has a significant impact on the reliability of computational tasks on NISQ devices. To improve the overall fidelity of physical circuits, we propose a quantum circuit mapping method based on multi-agent cooperation. This approach considers the Spatio-temporal variation of quantum operation quality on the NISQ device when inserting ancillary operation. It consists of two core components: the qubit placement algorithm and the qubit routing method. The qubit placement algorithm exploits the iterated local search framework to find a desirable initial mapping for the reduced symmetric form of the original circuit. The qubit routing method generates the physical circuit through multi-agent communication and collaboration. Each agent inserts the ancillary gates independently according to its environment state. The quality of the physical circuit evolves according to an information-exchanging mechanism between agents, which combines the local search and global search. To experiment on the benchmark circuits (with hundreds of quantum gates) beyond the capacity of current NISQ devices, we build a noisy simulator with gate error 10x lower than that of the latest NISQ device of IBM. The experimental results confirm the performance of our approach in improving circuit fidelity. Compared with the stateof-the-art method, our method can improve the success rate by 25.86% on average and 95.42% at maximum.

2111.09033v3

A Four-Party Quantum Secret-Sharing Scheme based on Grover's Search Algorithm

The work presents an amalgam of quantum search algorithm (QSA) and quantum secret sharing (QSS). The proposed QSS scheme utilizes Grover's three-particle quantum state. In this scheme, the dealer prepares an encoded state by encoding the classical information as a marked state and shares the states' qubits between three participants. The participants combine their qubits and find the marked state as a measurement result of the three-qubit state. The security analysis shows the scheme is stringent against malicious participants or eavesdroppers. In comparison to the existing schemes, our protocol fairs pretty well and has a high encoding capacity. The simulation analysis is done on the cloud platform IBM-QE thereby showing the practical feasibility of the scheme.

2111.08932v1

Predicting non-Markovian superconducting qubit dynamics from tomographic reconstruction

Non-Markovian noise presents a particularly relevant challenge in understanding and combating decoherence in quantum computers, yet is challenging to capture in terms of simple models. Here we show that a simple phenomenological dynamical model known as the post-Markovian master equation (PMME) accurately captures and predicts non-Markovian noise in a superconducting qubit system. The PMME is constructed using experimentally measured state dynamics of an IBM Quantum Experience cloud-based quantum processor, and the model thus constructed successfully predicts the non-Markovian dynamics observed in later experiments. The model also allows the extraction of information about cross-talk and measures of non-Markovianity. We demonstrate definitively that the PMME model predicts subsequent dynamics of the processor better than the standard Markovian master equation.

2111.07051v1

The Present and Future of Discrete Logarithm Problems on Noisy Quantum Computers

The discrete logarithm problem (DLP) is the basis for several cryptographic primitives. Since Shor's work, it has been known that the DLP can be solved by combining a polynomial-size quantum circuit and a polynomial-time classical post-processing algorithm. Evaluating and predicting the instance size that quantum devices can solve is an emerging research topic. In this paper, we propose a quantitative measure based on the success probability of the post-processing algorithm to determine whether an experiment on a quantum device (or a classical simulator) succeeded. We also propose a procedure to modify bit strings observed from a Shor circuit to increase the success probability of a lattice-based post-processing algorithm. We report preliminary experiments conducted on IBM-Quantum quantum computers and near-future predictions based on noisy-device simulations. We conducted our experiments with the ibm_kawasaki device and discovered that the simplest circuit (7 qubits) from a 2-bit DLP instance achieves a sufficiently high success probability to proclaim the experiment successful. Experiments on another circuit from a slightly harder 2-bit DLP instance, on the other hand, did not succeed, and we determined that reducing the noise level by half is required to achieve a successful experiment. Finally, we give a near-term prediction based on required noise levels to solve some selected small DLP and integer factoring instances.

2111.06102v1

String Abstractions for Qubit Mapping

One of the key compilation steps in Quantum Computing (QC) is to determine an initial logical to physical mapping of the qubits used in a quantum circuit. The impact of the starting qubit layout can vastly affect later scheduling and placement decisions of QASM operations, yielding higher values on critical performance metrics (gate count and circuit depth) as a result of quantum compilers introducing SWAP operations to meet the underlying physical neighboring and connectivity constraints of the quantum device. In this paper we introduce a novel qubit mapping approach, string-based qubit mapping. The key insight is to prioritize the mapping of logical qubits that appear in longest repeating non-overlapping substrings of qubit pairs accessed. This mapping method is complemented by allocating qubits according to their global frequency usage. We evaluate and compare our new mapping scheme against two quantum compilers (QISKIT and TKET) and two device topologies, the IBM Manhattan (65 qubits) and the IBM Kolkata (27 qubits). Our results demonstrate that combining both mapping mechanisms often achieve better results than either one individually, allowing us to best QISKIT and TKET baselines, yielding between 13% and 17% average improvement in several group sizes, up to 32% circuit depth reduction and 63% gate volume improvement.

2111.03716v1

Experimenting quantum phenomena on NISQ computers using high level quantum programming

We execute the quantum eraser, the Elitzur-Vaidman bomb, and the Hardy's paradox experiment using high-level programming language on a generic, gate-based superconducting quantum processor made publicly available by IBM. The quantum circuits for these experiments use a mixture of one-qubit and multi-qubit gates and require high entanglement gate accuracy. The results aligned with theoretical predictions of quantum mechanics to high confidence on circuits using up to 3 qubits. The power of quantum computers and high-level language as a platform for experimenting and studying quantum phenomena is henceforth demonstrated.

2111.02896v2

Enabling a Programming Environment for an Experimental Ion Trap Quantum Testbed

Ion trap quantum hardware promises to provide a computational advantage over classical computing for specific problem spaces while also providing an alternative hardware implementation path to cryogenic quantum systems as typified by IBM's quantum hardware. However, programming ion trap systems currently requires both strategies to mitigate high levels of noise and also tools to ease the challenge of programming these systems with pulse- or gate-level operations. This work focuses on improving the state-of-the-art for quantum programming of ion trap testbeds through the use of a quantum language specification, QCOR, and by demonstrating multi-level optimizations at the language, intermediate representation, and hardware backend levels. We implement a new QCOR/XACC backend to target a general ion trap testbed and then demonstrate the usage of multi-level optimizations to improve circuit fidelity and to reduce gate count. These techniques include the usage of a backend-specific numerical optimizer and physical gate optimizations to minimize the number of native instructions sent to the hardware. We evaluate our compiler backend using several QCOR benchmark programs, finding that on present testbed hardware, our compiler backend maintains the number of two-qubit native operations but decreases the number of single-qubit native operations by 1.54 times compared to the previous compiler regime. For projected testbed hardware upgrades, our compiler sees a reduction in two-qubit native operations by 2.40 times and one-qubit native operations by 6.13 times.

2111.00146v2

Separation of gates in quantum parallel programming

The number of qubits in current quantum computers is a major restriction on their wider application. To address this issue, Ying conceived of using two or more small-capacity quantum computers to produce a larger-capacity quantum computing system by quantum parallel programming ([M. S. Ying, Morgan-Kaufmann, 2016]). In doing so, the main obstacle is separating the quantum gates in the whole circuit to produce a tensor product of the local gates. In this study, we theoretically analyse the (sufficient and necessary) separability conditions of multipartite quantum gates in finite or infinite dimensional systems. We then conduct separation experiments with n-qubit quantum gates on IBM quantum computers using QSI software.

2110.14965v1

Quality, Speed, and Scale: three key attributes to measure the performance of near-term quantum computers

Defining the right metrics to properly represent the performance of a quantum computer is critical to both users and developers of a computing system. In this white paper, we identify three key attributes for quantum computing performance: quality, speed, and scale. Quality and scale are measured by quantum volume and number of qubits, respectively. We propose a speed benchmark, using an update to the quantum volume experiments that allows the measurement of Circuit Layer Operations Per Second (CLOPS) and identify how both classical and quantum components play a role in improving performance. We prescribe a procedure for measuring CLOPS and use it to characterize the performance of some IBM Quantum systems.

2110.14108v2

Demonstration of the Rodeo Algorithm on a Quantum Computer

The rodeo algorithm is an efficient algorithm for eigenstate preparation and eigenvalue estimation for any observable on a quantum computer. This makes it a promising tool for studying the spectrum and structure of atomic nuclei as well as other fields of quantum many-body physics. The only requirement is that the initial state has sufficient overlap probability with the desired eigenstate. While it is exponentially faster than well-known algorithms such as phase estimation and adiabatic evolution for eigenstate preparation, it has yet to be implemented on an actual quantum device. In this work, we apply the rodeo algorithm to determine the energy levels of a random one-qubit Hamiltonian, resulting in a relative error of $0.08\%$ using mid-circuit measurements on the IBM Q device Casablanca. This surpasses the accuracy of directly-prepared eigenvector expectation values using the same quantum device. We take advantage of the high-accuracy energy determination and use the Hellmann-Feynman theorem to compute eigenvector expectation values for a different random one-qubit observable. For the Hellmann-Feynman calculations, we find a relative error of $0.7\%$. We conclude by discussing possible future applications of the rodeo algorithm for multi-qubit Hamiltonians.

2110.07747v2

Deterministic Entanglement Distribution on Series-Parallel Quantum Networks

The performance of distributing entanglement between two distant nodes in a large-scale quantum network (QN) of partially entangled bipartite pure states is generally benchmarked against the classical entanglement percolation (CEP) scheme. Improvements beyond CEP were only achieved by nonscalable strategies for restricted QN topologies. This paper explores and amplifies a new and more effective mapping of a QN, referred to as concurrence percolation theory (ConPT), that suggests using deterministic rather than probabilistic protocols for scalably improving on CEP across arbitrary QN topologies. More precisely, we implement ConPT via a deterministic entanglement transmission (DET) scheme that is fully analogous to resistor network analysis, with the corresponding series and parallel rules represented by deterministic entanglement swapping and concentration protocols, respectively. The main contribution of this paper is to establish a powerful mathematical framework, which is applicable to arbitrary d-dimensional information carriers (qudits), that provides different natural optimality metrics in terms of generalized k-concurrences (a family of fundamental entanglement measures) for different QN topology. In particular, we conclude that the introduced DET scheme (a) is optimal over the well-known nested repeater protocol for distilling entanglement from partially entangled qubits and (b) leads to higher success probabilities of obtaining a maximally entangled state than using CEP. The implementation of the DET scheme is experimentally feasible as tested on IBM's quantum computation platform.

2110.04981v3

Experimentally accessible non-separability criteria for multipartite entanglement structure detection

The description of the complex separability structure of quantum states in terms of partially ordered sets has been recently put forward. In this work, we address the question of how to efficiently determine these structures for unknown states. We propose an experimentally accessible and scalable iterative methodology that identifies, on solid statistical grounds, sufficient conditions for non-separability with respect to certain partitions. In addition, we propose an algorithm to determine the minimal partitions (those that do not admit further splitting) consistent with the experimental observations. We test our methodology experimentally on a 20-qubit IBM quantum computer by inferring the structure of the 4-qubit Smolin and an 8-qubit W states. In the first case, our results reveal that, while the fidelity of the state is low, it nevertheless exhibits the partitioning structure expected from the theory. In the case of the W state, we obtain very disparate results in different runs on the device, which range from non-separable states to very fragmented minimal partitions with little entanglement in the system. Furthermore, our work demonstrates the applicability of informationally complete POVM measurements for practical purposes on current NISQ devices.

2110.04177v1

Qubit-efficient encoding scheme for quantum simulations of electronic structure

Simulating electronic structure on a quantum computer requires encoding of fermionic systems onto qubits. Common encoding methods transform a fermionic system of $N$ spin-orbitals into an $N$-qubit system, but many of the fermionic configurations do not respect the required conditions and symmetries of the system so the qubit Hilbert space in this case may have unphysical states and thus can not be fully utilized. We propose a generalized qubit-efficient encoding (QEE) scheme that requires the qubit number to be only logarithmic in the number of configurations that satisfy the required conditions and symmetries. For the case of considering only the particle-conserving and singlet configurations, we reduce the qubit count to an upper bound of $\mathcal O(m\log_2N)$, where $m$ is the number of particles. This QEE scheme is demonstrated on an H$_2$ molecule in the 6-31G basis set and a LiH molecule in the STO-3G basis set using fewer qubits than the common encoding methods. We calculate the ground-state energy surfaces using a variational quantum eigensolver algorithm with a hardware-efficient ansatz circuit. We choose to use a hardware-efficient ansatz since most of the Hilbert space in our scheme is spanned by desired configurations so a heuristic search for an eigenstate is sensible. The simulations are performed on IBM Quantum machines and the Qiskit simulator with a noise model implemented from a IBM Quantum machine. Using the methods of measurement error mitigation and error-free linear extrapolation, we demonstrate that most of the distributions of the extrapolated energies using our QEE scheme agree with the exact results obtained by Hamiltonian diagonalization in the given basis sets within chemical accuracy. Our proposed scheme and results show the feasibility of quantum simulations for larger molecular systems in the noisy intermediate-scale quantum (NISQ) era.

2110.04112v3

Variational determination of multi-qubit geometrical entanglement in NISQ computers

Current noise levels in physical realizations of qubits and quantum operations limit the applicability of conventional methods to characterize entanglement. In this adverse scenario, we follow a quantum variational approach to estimate the geometric measure of entanglement of multiqubit pure states. The algorithm requires only single-qubit gates and measurements, so it is well suited for NISQ devices. This is demonstrated by successfully implementing the method on IBM Quantum devices for Greenberger-Horne-Zeilinger states of $3$, $4$, and $5$ qubits. Numerical simulations with random states show the robustness and accuracy of the method. The scalability of the protocol is numerically demonstrated via matrix product states techniques up to $25$ qubits.

2110.03709v2

Coarse grained intermolecular interactions on quantum processors

Variational quantum algorithms (VQAs) are increasingly being applied in simulations of strongly-bound (covalently bonded) systems using full molecular orbital basis representations. The application of quantum computers to the weakly-bound intermolecular and non-covalently bonded regime however has remained largely unexplored. In this work, we develop a coarse-grained representation of the electronic response that is ideally suited for determining the ground state of weakly interacting molecules using a VQA. We require qubit numbers that grow linearly with the number of molecules and derive scaling behaviour for the number of circuits and measurements required, which compare favourably to traditional variational quantum eigensolver methods. We demonstrate our method on IBM superconducting quantum processors and show its capability to resolve the dispersion energy as a function of separation for a pair of non-polar molecules - thereby establishing a means by which quantum computers can model Van der Waals interactions directly from zero-point quantum fluctuations. Within this coarse-grained approximation, we conclude that current-generation quantum hardware is capable of probing energies in this weakly bound but nevertheless chemically ubiquitous and biologically important regime. Finally, we perform experiments on simulated and real quantum computers for systems of three, four and five oscillators as well as oscillators with anharmonic onsite binding potentials; the consequences of the latter are unexamined in large systems using classical computational methods but can be incorporated here with low computational overhead.

2110.00968v2

Towards the real-time evolution of gauge-invariant $\mathbb{Z}_2$ and $U(1)$ quantum link models on NISQ Hardware with error-mitigation

Practical quantum computing holds clear promise in addressing problems not generally tractable with classical simulation techniques, and some key physically interesting applications are those of real-time dynamics in strongly coupled lattice gauge theories. In this article, we benchmark the real-time dynamics of $\mathbb{Z}_2$ and $U(1)$ gauge invariant plaquette models using noisy intermediate scale quantum (NISQ) hardware, specifically the superconducting-qubit-based quantum IBM Q computers. We design quantum circuits for models of increasing complexity and measure physical observables such as the return probability to the initial state, and locally conserved charges. NISQ hardware suffers from significant decoherence and corresponding difficulty to interpret the results. We demonstrate the use of hardware-agnostic error mitigation techniques, such as circuit folding methods implemented via the Mitiq package, and show what they can achieve within the quantum volume restrictions for the hardware. Our study provides insight into the choice of Hamiltonians, construction of circuits, and the utility of error mitigation methods to devise large-scale quantum computation strategies for lattice gauge theories.

2109.15065v3

Divide-and-conquer verification method for noisy intermediate-scale quantum computation

Several noisy intermediate-scale quantum computations can be regarded as logarithmic-depth quantum circuits on a sparse quantum computing chip, where two-qubit gates can be directly applied on only some pairs of qubits. In this paper, we propose a method to efficiently verify such noisy intermediate-scale quantum computation. To this end, we first characterize small-scale quantum operations with respect to the diamond norm. Then by using these characterized quantum operations, we estimate the fidelity $\langle\psi_t|\hat{\rho}_{\rm out}|\psi_t\rangle$ between an actual $n$-qubit output state $\hat{\rho}_{\rm out}$ obtained from the noisy intermediate-scale quantum computation and the ideal output state (i.e., the target state) $|\psi_t\rangle$. Although the direct fidelity estimation method requires $O(2^n)$ copies of $\hat{\rho}_{\rm out}$ on average, our method requires only $O(D^32^{12D})$ copies even in the worst case, where $D$ is the denseness of $|\psi_t\rangle$. For logarithmic-depth quantum circuits on a sparse chip, $D$ is at most $O(\log{n})$, and thus $O(D^32^{12D})$ is a polynomial in $n$. By using the IBM Manila 5-qubit chip, we also perform a proof-of-principle experiment to observe the practical performance of our method.

2109.14928v3

Hexagonal matching codes with 2-body measurements

Matching codes are stabilizer codes based on Kitaev's honeycomb lattice model. The hexagonal form of these codes are particularly well-suited to the heavy-hexagon device layouts currently pursued in the hardware of IBM Quantum. Here we show how the stabilizers of the code can be measured solely through the 2-body measurements that are native to the architecture. The process is then run on 27 and 65 qubit devices, to compare results with simulations for a standard error model. It is found that the results correspond well to simulations where the noise strength is similar to that found in the benchmarking of the devices. The best devices show results consistent with a noise model with an error probability of around $1.5\%-2\%$.

2109.13308v2

Faster and More Reliable Quantum SWAPs via Native Gates

Due to the sparse connectivity of superconducting quantum computers, qubit communication via SWAP gates accounts for the vast majority of overhead in quantum programs. We introduce a method for improving the speed and reliability of SWAPs at the level of the superconducting hardware's native gateset. Our method relies on four techniques: 1) SWAP Orientation, 2) Cross-Gate Pulse Cancellation, 3) Commutation through Cross-Resonance, and 4) Cross-Resonance Polarity. Importantly, our Optimized SWAP is bootstrapped from the pre-calibrated gates, and therefore incurs zero calibration overhead. We experimentally evaluate our optimizations with Qiskit Pulse on IBM hardware. Our Optimized SWAP is 11% faster and 13% more reliable than the Standard SWAP. We also experimentally validate our optimizations on application-level benchmarks. Due to (a) the multiplicatively compounding gains from improved SWAPs and (b) the frequency of SWAPs, we observe typical improvements in success probability of 10-40%. The Optimized SWAP is available through the SuperstaQ platform.

2109.13199v1

Detection of energy levels of a spin system on a quantum computer by probe spin evolution

We propose a method for detection of energy levels of arbitrary spin system on a quantum computer based on studies of evolution of only one probe spin. On the basis of the proposed method energy levels of spin systems are found on IBM's quantum computer ibmq-bogota, among them are spin chain in magnetic field, triangle spin cluster, Ising model on squared lattice in magnetic field. The results of quantum calculations are in agreement with the theoretical ones. The method is efficient for estimation of the energy levels of many-spin systems and opens a possibility to achieve quantum supremacy in solving eigenvalue problem with development of multi-qubit quantum computers.

2109.11400v2

JigSaw: Boosting Fidelity of NISQ Programs via Measurement Subsetting

Near-term quantum computers contain noisy devices, which makes it difficult to infer the correct answer even if a program is run for thousands of trials. On current machines, qubit measurements tend to be the most error-prone operations (with an average error-rate of 4%) and often limit the size of quantum programs that can be run reliably on these systems. As quantum programs create and manipulate correlated states, all the program qubits are measured in each trial and thus, the severity of measurement errors increases with the program size. The fidelity of quantum programs can be improved by reducing the number of measurement operations. We present JigSaw, a framework that reduces the impact of measurement errors by running a program in two modes. First, running the entire program and measuring all the qubits for half of the trials to produce a global (albeit noisy) histogram. Second, running additional copies of the program and measuring only a subset of qubits in each copy, for the remaining trials, to produce localized (higher fidelity) histograms over the measured qubits. JigSaw then employs a Bayesian post-processing step, whereby the histograms produced by the subset measurements are used to update the global histogram. Our evaluations using three different IBM quantum computers with 27 and 65 qubits show that JigSaw improves the success rate on average by 3.6x and up-to 8.4x. Our analysis shows that the storage and time complexity of JigSaw scales linearly with the number of qubits and trials, making JigSaw applicable to programs with hundreds of qubits.

2109.05314v1

ADAPT: Mitigating Idling Errors in Qubits via Adaptive Dynamical Decoupling

The fidelity of applications on near-term quantum computers is limited by hardware errors. In addition to errors that occur during gate and measurement operations, a qubit is susceptible to idling errors, which occur when the qubit is idle and not actively undergoing any operations. To mitigate idling errors, prior works in the quantum devices community have proposed Dynamical Decoupling (DD), that reduces stray noise on idle qubits by continuously executing a specific sequence of single-qubit operations that effectively behave as an identity gate. Unfortunately, existing DD protocols have been primarily studied for individual qubits and their efficacy at the application-level is not yet fully understood. Our experiments show that naively enabling DD for every idle qubit does not necessarily improve fidelity. While DD reduces the idling error-rates for some qubits, it increases the overall error-rate for others due to the additional operations of the DD protocol. Furthermore, idling errors are program-specific and the set of qubits that benefit from DD changes with each program. To enable robust use of DD, we propose Adaptive Dynamical Decoupling (ADAPT), a software framework that estimates the efficacy of DD for each qubit combination and judiciously applies DD only to the subset of qubits that provide the most benefit. ADAPT employs a Decoy Circuit, which is structurally similar to the original program but with a known solution, to identify the DD sequence that maximizes the fidelity. To avoid the exponential search of all possible DD combinations, ADAPT employs a localized algorithm that has linear complexity in the number of qubits. Our experiments on IBM quantum machines (with 16-27 qubits) show that ADAPT improves the application fidelity by 1.86x on average and up-to 5.73x compared to no DD and by 1.2x compared to DD on all qubits.

2109.05309v1