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

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