公开(公告)号：US20250053849A1

公开(公告)日：2025-02-13

申请号：US18785890

申请日：2024-07-26

Applicant: Google LLC

Inventor： Thomas Eugene O'Brien ,  Rolando Diego Somma ,  Ryan Babbush

IPC: G06N10/80 ,  G06N10/40 ,  G06N10/60

Abstract: Methods, systems, and apparatus for quantum simulation of a quantum system. In one aspect, a method includes, for an observable generated from a set of observables, wherein a commutator of each observable in the set of observables with the first Hamiltonian is equal to a combination of observables in the set of observables: encoding, by a quantum computer, a vector of coefficients of a time-dependent representation of the observable in a quantum state of a register of qubits; simulating, by the quantum computer, time evolution of the quantum state under a second Hamiltonian to obtain an evolved quantum state, wherein the second Hamiltonian comprises a matrix of complex weights in the linear combination of observables; measuring, by the quantum computer, the evolved quantum state; and post-processing, by a classical processor, obtained measurement results to obtain an expectation value of the observable.

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

IPC: G06N10/60 ,  G06N10/40

CPC classification number: G06N10/60 ,  G06N10/40

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.