Automated quantum programming via reinforcement learning for combinatorial optimization

2019-08-21 · Keri A. McKiernan, Erik Davis, M. Sohaib Alam, Chad Rigetti

We develop a general method for incentive-based programming of hybrid quantum-classical computing systems using reinforcement learning, and apply this to solve combinatorial optimization problems on both simulated and real gate-based quantum computers. Relative to a set of randomly generated problem instances, agents trained through reinforcement learning techniques are capable of producing short quantum programs which generate high quality solutions on both types of quantum resources. We observe generalization to problems outside of the training set, as well as generalization from the simulated quantum resource to the physical quantum resource.

📄 PDF Abstract BibTeX arXiv:1908.08054

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rigetti/gym-forest

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Combinatorial Optimizationreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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