Efficient Quantum Circuit Compression using Reinforcement Learning
Date
2021-05
Authors
Berthusen, Noah
Major Professor
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Orth, Peter
Committee Member
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Abstract
Computations by the current generation of noisy intermediate scale quantum (NISQ) computers are often plagued by errors such as decoherence and cross talk. Such errors severely limit the depth of NISQ quantum circuits, yet many quantum algorithms that show promise of a quantum speedup require deep circuits and prolonged coherence times. In this work, we propose leveraging Reinforcement Learning (RL) to intelligently build quantum circuits that can recreate given target states, given no information about the circuit used to construct them. The RL agent learns about the hidden system by receiving rewards based on local observables, calculated using the target state, and the fidelity of the final state. By constraining the depth of the circuits built by the agent, we hypothesize that this approach allows us to compress the depth of quantum circuits necessary to create the target state. One important application of our method is dynamic quantum simulation, where the target state is a time-evolved state using a given Hamiltonian and a Trotterized quantum circuit. Our method promises quantum simulations out to longer final times than are currently feasible on NISQ devices.
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