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- W4384648617 abstract "The problem of decomposing an arbitrary Clifford element into a sequence of Clifford gates is known as Clifford synthesis. Drawing inspiration from similarities between this and the famous Rubik's Cube problem, we develop a machine learning approach for Clifford synthesis based on learning an approximation to the distance to the identity. This approach is probabilistic and computationally intensive. However, when a decomposition is successfully found, it often involves fewer gates than existing synthesis algorithms. Additionally, our approach is much more flexible than existing algorithms in that arbitrary gate sets, device topologies, and gate fidelities may incorporated, thus allowing for the approach to be tailored to a specific device." @default.
- W4384648617 created "2023-07-19" @default.
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- W4384648617 date "2023-07-17" @default.
- W4384648617 modified "2023-10-18" @default.
- W4384648617 title "A Rubik's Cube inspired approach to Clifford synthesis" @default.
- W4384648617 doi "https://doi.org/10.48550/arxiv.2307.08684" @default.
- W4384648617 hasPublicationYear "2023" @default.
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