Particle Swarm Optimization for Quantum Circuit Synthesis: Performance Analysis and Insights

TL;DR

This paper explores the use of particle swarm optimization (PSO) for quantum circuit synthesis, specifically solving the MaxOne problem, and compares its performance with genetic algorithms, providing insights into its efficiency and effectiveness.

Contribution

It introduces a novel PSO-based method for quantum circuit encoding and compares its performance with genetic algorithms, highlighting PSO's faster convergence.

Findings

PSO converges more quickly to optimal solutions than genetic algorithms.

Different PSO parameter settings affect learning ability and convergence speed.

The proposed PSO method effectively solves the MaxOne quantum circuit synthesis problem.

Abstract

This paper discusses how particle swarm optimization (PSO) can be used to generate quantum circuits to solve an instance of the MaxOne problem. It then analyzes previous studies on evolutionary algorithms for circuit synthesis. With a brief introduction to PSO, including its parameters and algorithm flow, the paper focuses on a method of quantum circuit encoding and representation as PSO parameters. The fitness evaluation used in this paper is the MaxOne problem. The paper presents experimental results that compare different learning abilities and inertia weight variations in the PSO algorithm. A comparison is further made between the PSO algorithm and a genetic algorithm for quantum circuit synthesis. The results suggest PSO converges more quickly to the optimal solution.