Quantum Acceleration of Evolutionary Algorithms

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper investigates the potential of quantum algorithms to accelerate evolutionary algorithms (EAs). Traditional EAs often suffer from slow convergence rates due to computationally intensive selection processes. This work proposes a novel approach to leverage quantum parallelism to significantly speed up the search process within an EA. Specifically, we map the core operations of an EA – selection, crossover, and mutation – onto a quantum circuit. By exploiting the inherent parallelism of quantum computation, we aim to achieve a substantial improvement in the efficiency and performance of the EA. The theoretical framework is presented, outlining the mapping strategies and the potential advantages. While no experimental validation is included in this document, this work serves as a foundational study for future research in hybrid quantum-classical optimization. The core claim is that utilizing quantum algorithms to accelerate the simulation of evolutionary algorithms can improve their efficiency and performance. The core mechanism is the mapping of selection, crossover, and mutation operations within an EA to quantum circuits for parallel computation. This approach addresses a potential computational bottleneck in traditional EAs and promises a breakthrough in optimization tasks.

DOI: 10.5281/zenodo.22140342

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