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公开(公告)号:US20230368064A1
公开(公告)日:2023-11-16
申请号:US17929604
申请日:2022-09-02
Applicant: Google LLC
Inventor: Thomas Eugene O'Brien , Vadim Smelyanskiy , Lev loffe , Yuan Su , Ryan Babbush
Abstract: Methods, systems, and apparatus for gradient-based quantum assisted Hamiltonian learning. In one aspect, a method includes obtaining, by a classical processor, multiple experimental data points, wherein each experimental data point is generated according to a Hamiltonian comprising parameters with unknown values; learning, by the classical processor, values of the parameters, comprising iteratively adjusting, by the classical processor and until predetermined completion criteria are met, estimated values of the parameters to minimize a cost function, wherein the cost function is dependent on the multiple experimental data points and at each iteration derivatives of the cost function with respect to respective estimated values of the parameters for the previous iteration are computed using a quantum computer.