	US 12,086,203 B2
	Noise reduced circuits for superconducting quantum computers
Omar Shehab, Hyattsville, MD (US); and Isaac Hyun Kim, Menlo Park, CA (US)
Assigned to IONQ, INC., College Park, MD (US)
Filed by IONQ, INC., College Park, MD (US)
Filed on Nov. 7, 2022, as Appl. No. 17/981,939.
Application 17/981,939 is a division of application No. 16/867,332, filed on May 5, 2020, granted, now 11,586,702.
Claims priority of provisional application 62/852,269, filed on May 23, 2019.
Prior Publication US 2023/0085177 A1, Mar. 16, 2023
Int. Cl. G06F 17/11 (2006.01); G06N 10/00 (2022.01); H10N 60/12 (2023.01)

CPC G06F 17/11 (2013.01) [G06N 10/00 (2019.01); H10N 60/12 (2023.02)]

16 Claims

1. A method of performing computation in a hybrid quantum-classical computing system comprising a classical computer and a quantum processor, comprising:

computing, by the classical computer, a model Hamiltonian onto which a selected problem is mapped, wherein the model Hamiltonian comprises a plurality of sub-Hamiltonians;

executing iterations, each iteration comprising:

setting the quantum processor in an initial state, wherein the quantum processor comprises a plurality of Josephson junctions, each of which has two frequency-separated states defining a superconducting qubit;

transforming the quantum processor from the initial state to a trial state based on each of the plurality of sub-Hamiltonians and an initial set of variational parameters by applying a first trial state preparation circuit to the quantum processor;

measuring an expectation value of each of the plurality of sub-Hamiltonians on the quantum processor; and

selecting, by the classical computer, another set of variational parameters based on a classical optimization method, if a difference between the measured expectation values of the model Hamiltonian in a current iteration and a previous iteration is more than a predetermined value; and

outputting the measured expectation value of the model Hamiltonian as an optimized solution to the selected problem.