A gridworld benchmark shows amplitude-amplification QRL outperforms PQC and free-energy QRL on cost and clock time, while entanglement and replica-count ablations find little evidence that current QRL performance depends on genuinely quantum effects.
Free energy-based reinforcement learning using a quantum processor
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abstract
Recent theoretical and experimental results suggest the possibility of using current and near-future quantum hardware in challenging sampling tasks. In this paper, we introduce free energy-based reinforcement learning (FERL) as an application of quantum hardware. We propose a method for processing a quantum annealer's measured qubit spin configurations in approximating the free energy of a quantum Boltzmann machine (QBM). We then apply this method to perform reinforcement learning on the grid-world problem using the D-Wave 2000Q quantum annealer. The experimental results show that our technique is a promising method for harnessing the power of quantum sampling in reinforcement learning tasks.
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Benchmarking Quantum Reinforcement Learning
A gridworld benchmark shows amplitude-amplification QRL outperforms PQC and free-energy QRL on cost and clock time, while entanglement and replica-count ablations find little evidence that current QRL performance depends on genuinely quantum effects.