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Quantum-Inspired Optimization with Variational Apprenticeship

Jincheng Zhang

Published Sep 1, 2026• 0 citations
Publisher

Abstract

Quantum-Inspired Optimization with Variational Apprenticeship presents a novel approach to optimization that leverages the principles of quantum computing while incorporating the strengths of variational methods. This technique focuses on developing a surrogate function, guided by a variational algorithm, to efficiently explore the solution space and improve the performance of optimization algorithms. The core mechanism involves utilizing a quantum-inspired surrogate to approximate the objective function, while concurrently optimizing the solution itself through a variational approach. This combination offers the potential for enhanced exploration, faster convergence, and improved robustness compared to traditional optimization methods. The study demonstrates the efficacy of this method through simulations and theoretical analysis, showcasing its potential for tackling complex optimization problems across diverse domains.

DOI
10.5281/zenodo.22219237

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