Invention Grant
- Patent Title: Quantum feature kernel alignment
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Application No.: US16374354Application Date: 2019-04-03
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Publication No.: US11748665B2Publication Date: 2023-09-05
- Inventor: Jay M. Gambetta , Jennifer Ranae Glick , Paul Kristan Temme , Tanvi Pradeep Gujarati
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee Address: US NY Armonk
- Agency: Garg Law Firm, PLLC
- Agent Rakesh Garg; Erik Johnson
- Main IPC: G06N20/10
- IPC: G06N20/10 ; G06N10/00

Abstract:
The illustrative embodiments provide a method, system, and computer program product for quantum feature kernel alignment using a hybrid classical-quantum computing system. An embodiment of a method for hybrid classical-quantum decision maker training includes receiving a training data set. In an embodiment, the method includes selecting, by a first processor, a sampling of objects from the training set, each object represented by at least one vector.
In an embodiment, the method includes applying, by a quantum processor, a set of quantum feature maps to the selected objects, the set of quantum maps corresponding to a set of quantum kernels. In an embodiment, the method includes evaluating, by a quantum processor, a set of parameters for a quantum feature map circuit corresponding to at least one of the set of quantum feature maps.
In an embodiment, the method includes applying, by a quantum processor, a set of quantum feature maps to the selected objects, the set of quantum maps corresponding to a set of quantum kernels. In an embodiment, the method includes evaluating, by a quantum processor, a set of parameters for a quantum feature map circuit corresponding to at least one of the set of quantum feature maps.
Public/Granted literature
- US20200320437A1 QUANTUM FEATURE KERNEL ALIGNMENT Public/Granted day:2020-10-08
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