Quantum Boltzmann Machines for Sample-Efficient Reinforcement Learning

Thore Gerlach; Michael Schenk; Verena Kain

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arXiv · 2511.04856

Quantum Boltzmann Machines for Sample-Efficient Reinforcement Learning

Thore Gerlach·Michael Schenk·Verena Kain

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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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Thore Gerlach, Michael Schenk, Verena Kain. 2025-11-06. Quantum Boltzmann Machines for Sample-Efficient Reinforcement Learning. https://arxiv.org/abs/2511.04856

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