Title: Quantum-Inspired Optimization via Variational Autoencoders

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-inspired optimization represents a burgeoning field aiming to leverage quantum mechanics principles to enhance the efficiency and effectiveness of traditional optimization algorithms. This paper proposes a novel technique, Quantum-Guided VAEs, which utilizes variational autoencoders (VAEs) to intelligently explore the solution space of complex optimization problems. The core mechanism focuses on employing the VAE's probabilistic nature to guide the search process, prioritizing exploration of less-visited regions and significantly accelerating the optimization process. This research offers a promising avenue for tackling challenging optimization scenarios, potentially bridging the gap between classical and quantum computing paradigms.

DOI: 10.5281/zenodo.22190382

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