Parametric Synthesis of Quantum Circuits for Training Perceptron Neural
  Networks
This paper showcases a method of parametric synthesis of quantum circuits for
training perceptron neural networks. Synapse weights are found using Grover's
algorithm with a modified oracle function. The results of running these
parametrically synthesized circuits for training perceptrons of three different
topologies are described. The circuits were run on a 100-qubit IBM quantum
simulator. The synthesis of quantum circuits is carried out using quantum
synthesizer "Naginata", which was developed in the scope of this work, the
source code of which is published and further documented on GitHub. The article
describes the quantum circuit synthesis algorithm for training single-layer
perceptrons. At the moment, quantum circuits are created mainly by manually
placing logic elements on lines that symbolize quantum bits. The purpose of
creating Quantum Circuit Synthesizer "Naginata" was due to the fact that even
with a slight increase in the number of operations in a quantum algorithm,
leads to the significant increase in size of the corresponding quantum circuit.
This causes serious difficulties both in creating and debugging these quantum
circuits. The purpose of our quantum synthesizer is enabling users an
opportunity to implement quantum algorithms using higher-level commands. This
is achieved by creating generic blocks for frequently used operations such as:
the adder, multiplier, digital comparator (comparison operator), etc. Thus, the
user could implement a quantum algorithm by using these generic blocks, and the
quantum synthesizer would create a suitable circuit for this algorithm, in a
format that is supported by the chosen quantum computation environment. This
approach greatly simplifies the processes of development and debugging a
quantum algorithm.
Cesar Borisovich Pronin Andrey Vladimirovich Ostroukh