Quantum-Inspired Optimization with Hamming-Distance Reinforcement for Hypercube-Encoded Reversible Circuit Synthesis

Abstract

: Quantum logic reversible synthesis is a fundamental operation in quantum computing. One of the most challenging issues in this field resides in navigating the immense search space to synthesize the most compact circuit configurations, which are critical for realizing reliable, noise-free, and error-free quantum computing systems. To address this challenge, this study proposes a novel hypercube-encoded quantum-inspired optimization framework to formulate the synthesis task as a trajectory-finding process. This structure-informed domain knowledge transformation delivers exceptional search direction guidance, moving away from blind, black-box exploration. Specifically, by mapping the reversible functions onto the hypercube architecture, the framework embeds explicit dual Hamming-distance (HMD) guidance metrics into a global-best guided quantum-inspired tabu search (GQTS) engine. To minimize computational cost and enhance search efficiency, the framework incorporates a domain-informed initialization and couples a streamlined two-particle configuration with a global-best mechanism, thereby amplifying the efficiency of the underlying quantum-inspired updating mechanism to escape local optima and rapidly converge once a successfully synthesized superior path is locked. Under an online step relaxation mechanism, the framework preserves exceptional structural optimization flexibility without altering the underlying hypercube representation. The framework’s significance is rigorously evaluated against both rigid hypercube rule-based methods and generic randomized search-based heuristics, using gate count and exact-optimality as primary evaluation metrics. Extensive ablation studies first validate the individual and synergistic contributions of each core algorithmic component. Experimental results demonstrate that for the complete set of 3-bit reversible functions, the proposed HMD-guided GQTS (HMD-GQTS) framework achieves over 98% exact-optimal circuits in a single fine-tuned sweep, with selective retries attaining 100% exhaustive optimal coverage. Furthermore, for typical 4-bit benchmark instances, the method consistently delivers competitive gate counts, matching or improving upon previous hypercube-based outcomes. Through the seamless integration of structure-informed guidance and coordinated optimization mechanisms, the framework preserves exceptional structural flexibility and algorithmic robustness, offering an effective, low-cost, and highly scalable avenue for reversible circuit synthesis.