Parametric Synthesis of Computational Circuits for Complex Quantum Algorithms

TL;DR

This paper introduces Naginata, a quantum circuit synthesizer that simplifies the creation and debugging of complex quantum algorithms by allowing higher-level command usage and generic blocks, with applications in quantum machine learning.

Contribution

The paper presents a novel quantum circuit synthesizer that automates circuit generation from high-level commands, improving development efficiency and enabling practical quantum algorithm implementation.

Findings

Enables implementation of quantum algorithms with higher-level commands.

Creates generic blocks for common quantum operations like adders and multipliers.

Facilitates quantum circuit creation for neural network training.

Abstract

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. The proposed approach for implementing quantum algorithms has a potential application in the field of machine learning, in this regard, we provided an example of creating a circuit for training a simple neural network. Neural networks have a significant impact on the technological development of the transport and road complex, and there is a potential for improving the reliability and efficiency of their learning process by utilizing quantum computation, through the introduction of quantum computing.