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Optimal Quantum Programming from Reinforcement Learning
Alam, Sohaib - Rigetti Computing
Presentation on Thursday, Dec. 12, 2019, noon
Location: MIT Room 6C-442
Reinforcement learning has been used across a wide variety of disciplines to discover optimal sequences of actions, or policies, through a reward based mechanism. With a suitable identification of an agent-environment interaction, we can adopt this framework to generate optimal quantum circuits for specific programming tasks. In this talk, I will discuss how we can apply this approach to solve combinatorial optimization problems on near-term quantum devices. I will further discuss how we can use the related technique of dynamic programming to find optimally short gate sequences for state preparation as well as quantum compilation.