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arXiv · 1706.00074

Free energy-based reinforcement learning using a quantum processor

Anna Levit·Daniel Crawford·Navid Ghadermarzy·Jaspreet S. Oberoi·Ehsan Zahedinejad·Pooya Ronagh
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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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Anna Levit, Daniel Crawford, Navid Ghadermarzy, Jaspreet S. Oberoi, Ehsan Zahedinejad, Pooya Ronagh. 2017-05-29. Free energy-based reinforcement learning using a quantum processor. https://arxiv.org/abs/1706.00074

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