Probability Logic Quantum Computing: Adaptive Evolution Algorithm

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel adaptive evolution algorithm for training quantum probabilistic logic circuits, designed to facilitate rapid iteration and optimization of quantum computations. The algorithm leverages the principles of evolutionary algorithms to dynamically adjust circuit parameters, enabling the circuit to adapt to varying input conditions and improve overall performance. The core mechanism centers around optimizing the circuit's configuration through a self-adaptive evolutionary process, providing a flexible and automated approach to quantum circuit design. This work addresses the limitations of conventional quantum algorithms by introducing a method that can dynamically adjust parameters in response to environmental changes, ultimately enabling faster and more robust quantum computations. The paper discusses the design of the evolutionary process, the impact of parameter tuning, and the resulting performance improvements observed through simulations.

DOI: 10.5281/zenodo.22205373

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