Invention Grant
- Patent Title: Refining qubit calibration models using supervised learning
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Application No.: US16772387Application Date: 2017-12-15
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Publication No.: US11556813B2Publication Date: 2023-01-17
- Inventor: Paul Klimov , Julian Shaw Kelly
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Agency: Fish & Richardson P.C.
- International Application: PCT/US2017/066766 WO 20171215
- International Announcement: WO2019/117955 WO 20190620
- Main IPC: G06N5/00
- IPC: G06N5/00 ; G06N5/04 ; G06N10/00 ; G06N20/00

Abstract:
A computer-implemented method for refining a qubit calibration model is described. The method comprises receiving, at a learning module, training data, wherein the training data comprises a plurality of calibration data sets, wherein each calibration data set is derived from a system comprising one or more qubits, and a plurality of parameter sets, each parameter set comprising extracted parameters obtained using a corresponding calibration data set, wherein extracting the parameters includes fitting a qubit calibration model to the corresponding calibration data set using a fitter algorithm. The method further comprises executing, at the learning module, a supervised machine learning algorithm which processes the training data to learn a perturbation to the qubit calibration model that captures one or more features in the plurality of calibration data sets that are not captured by the qubit calibration model, thereby to provide a refined qubit calibration model.
Public/Granted literature
- US20210081816A1 REFINING QUBIT CALIBRATION MODELS USING SUPERVISED LEARNING Public/Granted day:2021-03-18
Information query
IPC分类:
G | 物理 |
G06 | 计算;推算或计数 |
G06N | 基于特定计算模型的计算机系统 |
G06N5/00 | 利用基于知识的模式的计算机系统 |