Quantum State Space Learning Algorithm

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper introduces a novel quantum state space learning algorithm designed to enhance the efficiency of quantum systems through optimized parameter adjustment. Traditional approaches primarily rely on classical parameter tuning, limiting the potential for significant performance gains. This algorithm leverages the inherent properties of quantum state spaces—specifically, quantum entanglement and superposition—to dynamically learn and refine quantum system parameters, offering a fundamentally different approach to optimization. The core mechanism centers around constructing and manipulating a quantum state space representation to effectively guide the system towards optimal performance. This advancement holds significant promise for accelerating quantum computation and expanding the capabilities of quantum devices.

DOI: 10.5281/zenodo.22158809

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