Free energy-based reinforcement learning using a quantum processor

Anonymous

Nov 03, 2017 (modified: Nov 03, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • 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.
  • TL;DR: We train Quantum Boltzmann Machines using a quantum processor and simulated quantum annealing to perform a reinforcement learning task.
  • Keywords: Quantum Annealing, Reinforcement Learning, Boltzmann Machines, Markov Chain Monte Carlo

Loading