Multi-objective design of quantum circuits using genetic programming

TL;DR

This paper introduces a multi-objective genetic programming approach for quantum circuit design that optimizes depth, cost, and nearest neighbor metrics, incorporating a two-step fitness function and global phase equivalence.

Contribution

It is the first to apply a multi-objective genetic programming method considering multiple metrics and global phase in quantum circuit design.

Findings

Effective multi-objective optimization of quantum circuits.

Fast convergence to high-quality solutions.

Improved circuit metrics compared to traditional methods.

Abstract

Quantum computing is a new way of data processing based on the concept of quantum mechanics. Quantum circuit design is a process of converting a quantum gate to a series of basic gates and is divided into two general categories based on the decomposition and composition. In the second group, using evolutionary algorithms and especially genetic algorithms, multiplication of matrix gates was used to achieve the final characteristic of quantum circuit. Genetic programming is a subfield of evolutionary computing in which computer programs evolve to solve studied problems. In past research that has been done in the field of quantum circuits design, only one cost metrics (usually quantum cost) has been investigated. In this paper for the first time, a multi-objective approach has been provided to design quantum circuits using genetic programming that considers the depth and the cost of nearest neighbor metrics in addition to quantum cost metric. Another innovation of this article is the use of two-step fitness function and taking into account the equivalence of global phase in quantum gates. The results show that the proposed method is able to find a good answer in a short time.