Quantum Encoded Quantum Evolutionary Algorithm for the Design of Quantum Circuits

TL;DR

This paper introduces QEQEA, a quantum-encoded evolutionary algorithm for quantum circuit design, comparing its performance with classical GPU-accelerated genetic algorithms and exploring potential quantum advantages.

Contribution

The paper presents a quantum encoding scheme for evolutionary algorithms and evaluates its performance against classical methods in quantum circuit synthesis.

Findings

Quantum encoding offers potential for acceleration in quantum circuit search.

Quantum implementation shows some disadvantages compared to classical algorithms.

Quantum encoding could enable future quantum speedups despite current limitations.

Abstract

In this paper we present Quanrum Encoded Quantum Evolutionary Algorithm (QEQEA) and compare its performance against a a classical GPU accelerated Genetic Algorithm (GPUGA). The proposed QEQEA differs from existing quantum evolutionary algorithms in several points: representation of candidates circuits is using qubits and qutrits and the proposed evolutionary operators can in theory be implemented on quantum computer provided a classical control exists. The synthesized circuits are obtained by a set of measurements performed on the encoding units of quantum representation. Both algorithms are accelerated in GPGPU. The main target of this paper, is not to propose a completely novel quantum genetic algorithm but to rather experimentally estimate the advantages of certain components of genetic algorithm being encoded and implemented in a quantum compatible manner. The algorithms are compared and evaluated on several reversible and quantum circuits. The results demonstrate that on one hand the quantum encoding and quantum implementation compatible implementation provides certain disadvantages with respect to the classical evolutionary computation. On the other hand, encoding certain components in a quantum compatible manner could in theory allow to accelerate the search. This acceleration would in turn counter weight the implementation limitations.