Robustness And Generalization In Quantum Reinforcement Learning Via Lipschitz Regularization

Abstract

Quantum machine learning leverages quantum computing to enhance accuracy and reduce model complexity compared to classical approaches, promising significant advancements in various fields. Within this domain, quantum reinforcement learning has garnered attention, often realized using variational quantum circuits to approximate the policy function. This paper addresses the robustness and generalization of quantum reinforcement learning by combining principles from quantum computing and control theory. Leveraging recent results on robust quantum machine learning, we utilize Lipschitz bounds to propose a regularized version of a quantum policy gradient approach, named the RegQPG algorithm. We show that training with RegQPG improves the robustness and generalization of the resulting policies. Furthermore, we introduce an algorithmic variant that incorporates curriculum learning, which minimizes failures during training. Our findings are validated through numerical experiments, demonstratin

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keymeyer2024robustness

Related papers

• Quantum Natural Policy Gradients: Towards Sample-efficient Reinforcement Learning (2023)7.16
• A Survey On Quantum Reinforcement Learning (2022)0.00
• Hybrid Quantum-classical Policy Gradient For Adaptive Control Of Cyber-physical Systems: A Comparative Study Of VQC Vs. MLP (2025)0.00
• Variational Quantum Circuits For Deep Reinforcement Learning (2019)19.19
• Variational Quantum Soft Actor-critic (2021)0.00
• Quantum Policy Iteration Via Amplitude Estimation And Grover Search -- Towards Quantum Advantage For Reinforcement Learning (2022)0.00
• Quantum Algorithms For Reinforcement Learning With A Generative Model (2021)0.00
• Accelerating Quantum Reinforcement Learning With A Quantum Natural Policy Gradient Based Approach (2025)0.00