Free-Energy-based Reinforcement Learning Using a Quantum Processor

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

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.

Presented at: Theory of Quantum Computation, Communication and Cryptography TCQ 2017

PDF     arXiv preprint