US 12,321,838 B2
Quantum computing with kernel methods for machine learning
Jarrod Ryan McClean, Marina Del Rey, CA (US); and Hsin-Yuan Huang, Pasadena, CA (US)
Assigned to Google LLC, Mountain View, CA (US)
Filed by Google LLC, Mountain View, CA (US)
Filed on Oct. 19, 2021, as Appl. No. 17/505,202.
Claims priority of provisional application 63/093,611, filed on Oct. 19, 2020.
Prior Publication US 2022/0121998 A1, Apr. 21, 2022
Int. Cl. G06N 10/00 (2022.01); G06N 20/10 (2019.01)
CPC G06N 20/10 (2019.01) [G06N 10/00 (2019.01)] 24 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
obtaining, by a quantum computing device, a training dataset of quantum data points;
computing, by the quantum computing device, a kernel matrix that represents similarities among the quantum data points included in the training dataset, wherein the quantum data points are encoded as quantum states of qubits by the quantum computing device, comprising computing, for each pair of quantum data points in the training dataset, a corresponding value of a kernel function, wherein the kernel function is based on reduced density matrices for the quantum data points, wherein the kernel function takes a first and a second quantum data points as input and computes a similarity between the first and the second quantum data points as an element in the kernel matrix, and wherein the reduced density matrices are computed for quantum states of a subset of qubits; and
providing, by the quantum computing device, the kernel matrix to a classical processor.