Reinforcement Learning via Replica Stacking of Quantum Measurements for the Training of Quantum Boltzmann Machines

Anna Levit,  Daniel Crawford, Navid Ghadermarzy, Jaspreet S. Oberoi, Ehsan Zahedinejad, Pooya Ronagh

Feb 15, 2018 (modified: Feb 15, 2018) 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 replica stacking method and a quantum annealer to perform a reinforcement learning task.
  • Keywords: Quantum Annealing, Reinforcement Learning, Boltzmann Machines, Markov Chain Monte Carlo
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