Efficient Quantum Circuit Compression using Reinforcement Learning

dc.contributor.author Berthusen, Noah
dc.contributor.department Physics and Astronomy
dc.date.accessioned 2024-05-09T17:42:11Z
dc.date.available 2024-05-09T17:42:11Z
dc.date.issued 2021-05
dc.description.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.
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/aw4NZayr
dc.subject.disciplines DegreeDisciplines::Physical Sciences and Mathematics::Computer Sciences::Software Engineering
dc.title Efficient Quantum Circuit Compression using Reinforcement Learning
dc.type Presentation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 4a05cd4d-8749-4cff-96b1-32eca381d930
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