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Free Energy-Based Reinforcement Learning Using Quantum Monte Carlo and Quantum Annealing

Pooya Ronagh, Anna Levit, Daniel Crawford +3 more

Bulletin of the American Physical SocietyMar 7, 20182 citations

Classical Reinforcement Learning for Experimental Quantum Error Correction

Volodymyr Sivak, Henry X. Liu, Alec Eickbusch +3 more

Bulletin of the American Physical SocietyMar 17, 20210 citations

Reinforcement Learning-based Adaptive Error Correction for BB84 Quantum Key Distribution: A Q-Learning Approach to Post-Processing Optimization

Devishree Rohit Naidu, Sweta Jain, Ananya Anand +2 more

International Conference on Connected Intelligence for Industrial Applications (CI2A)Apr 3, 20260 citations

Quantum Annealing for Real-World Machine Learning Applications

Rajdeep Kumar Nath, Himanshu Thapliyal, Travis S. Humble

Springer eBooksAug 7, 20232 citations

Free energy-based reinforcement learning using a quantum processor

Anna Levit, Daniel Crawford, Navid Ghadermarzy +3 more

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.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.

arXiv:1706.00074May 29, 201716 citations

Integration of Machine Learning with Quantum Annealing

Hadi Salloum, Hamza Shafee Aldaghstany, Osama Orabi +3 more

Lecture notes on data engineering and communications technologiesJan 1, 202415 citations

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