Quantum Boltzmann Machines for Sample-Efficient Reinforcement Learning
Thore Gerlach ⋅ Michael Schenk ⋅ Verena Kain
Abstract
We propose Continuous Semi-Quantum Boltzmann Machines, extending previous free energy-based models to continuous-action reinforcement learning. We leverage exponential family priors and hybrid quantum-classical sampling to improve expressiveness and sample efficiency while reducing qubit requirements.
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