Dynamic Programming with Quantum-Inspired Search Algorithms

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper investigates the potential of accelerating dynamic programming (DP) algorithms by incorporating search strategies derived from quantum mechanics, specifically quantum tunneling and superposition. The core premise is that the inherent exploration capabilities of quantum phenomena can be simulated through quantum-inspired search algorithms, such as quantum annealing or evolutionary algorithms mimicking tunneling behavior, to efficiently navigate the solution space of DP problems. We propose a framework where a classical DP algorithm is augmented with a quantum-inspired search component. The aim is to leverage the strengths of both approaches – the precision and systematic nature of DP combined with the potentially faster exploration offered by quantum-inspired techniques. The effectiveness of this approach is evaluated through theoretical analysis and conceptual design, focusing on scenarios where traditional DP methods struggle with large state spaces or complex decision boundaries. This research contributes to a new paradigm in algorithm design, bridging the gap between classical and quantum computing by utilizing quantum-inspired methods to optimize classical algorithms. The core idea is to treat the DP problem as a tunneling event within a high-dimensional space, significantly reducing the time required to find optimal solutions.

DOI: 10.5281/zenodo.22221753

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