Quantum Boltzmann Machines for Sample-Efficient Reinforcement Learning

• URL: http://arxiv.org/abs/2511.04856v1
• Date: Thu, 06 Nov 2025 22:40:18 GMT
• Title: Quantum Boltzmann Machines for Sample-Efficient Reinforcement Learning
• Abstract summary: We introduce theoretically grounded Continuous Semi-Quantum Boltzmann Machines that support continuous-action reinforcement learning.<n>By combining exponential-family priors over visible units with Boltzmann distributions over hidden units, CSQBMs yield a hybrid quantum-classical model that reduces qubit requirements while retaining strong algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce theoretically grounded Continuous Semi-Quantum Boltzmann Machines (CSQBMs) that supports continuous-action reinforcement learning. By combining exponential-family priors over visible units with quantum Boltzmann distributions over hidden units, CSQBMs yield a hybrid quantum-classical model that reduces qubit requirements while retaining strong expressiveness. Crucially, gradients with respect to continuous variables can be computed analytically, enabling direct integration into Actor-Critic algorithms. Building on this, we propose a continuous Q-learning framework that replaces global maximization by efficient sampling from the CSQBM distribution, thereby overcoming instability issues in continuous control.

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