Parametric Synthesis of Quantum Circuits for Training Perceptron Neural Networks

TL;DR

This paper introduces a quantum circuit synthesis method using a new tool called Naginata, enabling easier implementation of perceptron training algorithms on quantum simulators with higher-level commands.

Contribution

The work presents Naginata, a quantum synthesizer that simplifies quantum circuit creation for perceptron training by using generic blocks, improving development and debugging processes.

Findings

Successfully trained perceptrons with three topologies on a 100-qubit simulator.

Demonstrated the effectiveness of Grover's algorithm with a modified oracle for synapse weight finding.

Published and documented the Naginata quantum synthesizer on GitHub.

Abstract

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.