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Quantum-Inspired Optimization for Reinforcement Learning Policy Gradient Methods

Jincheng Zhang

Published Sep 7, 2026• 0 citations
Publisher

Abstract

Reinforcement learning (RL) policy gradient methods, while effective, frequently encounter challenges related to instability and the sensitivity of their performance to the learning rate hyperparameter. This work introduces a novel approach leveraging quantum-inspired optimization techniques to address these issues. Specifically, we propose a quantum-inspired learning rate scheduler designed to dynamically adapt the update step size during policy gradient training. This scheduler, rooted in principles of quantum measurement and superposition, aims to provide a more robust and efficient learning process. The core claim of this paper is that the integration of this quantum-inspired learning rate scheduler significantly improves the stability and convergence speed of policy gradient methods. We present a theoretical framework and explore the potential of this approach, demonstrating its promise for enhancing RL performance. The proposed method offers a new direction in RL optimization, potentially bridging the gap between classical and quantum-inspired approaches.

DOI
10.5281/zenodo.22635240

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This paper investigates the potential of utilizing quantum-inspired optimization techniques to enhance reinforcement learning (RL) policies. Traditional RL algorithms often suffer from slow convergence and can be trapped in local optima. We propose leveraging quantum annealing, a prominent quantum computing paradigm, to optimize the policy parameters within an RL framework. The core idea is to map the policy optimization problem onto an Ising model, which is then solved using quantum annealing algorithms. This approach aims to provide a more efficient search process, potentially leading to faster learning rates and improved policy performance. We explore the theoretical underpinnings of this approach and present a conceptual framework for its implementation. The primary focus is on demonstrating the feasibility and potential benefits of this novel hybrid approach, rather than a full-fledged experimental validation due to the limitations of current quantum annealing hardware. The paper concludes with a discussion of future research directions and challenges.
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