US 2025/0165835 A1 · TRAINING A COMBINATION OF MULTIPLE QUANTUM AND CLASSICAL KERNELS

Abstract

A system to train multiple combined quantum classical kernels can comprise a memory that stores, and a processor that executes, computer executable components that perform operations comprising determining a set of kernel bandwidths, calculating a plurality of kernels, based on the kernel bandwidths, for subsets of features of a feature map, centering the plurality of kernels within a feature space of the feature map, regularizing parameters to combine the plurality of kernels, and combining the plurality of kernels into a combined kernel. Feature subsampling and data subsampling can be employed to compute a finite set subsampled kernels. Furthermore, the finite set of subsampled kernels can be combined classically to create a combined kernel that can represent an arbitrary target kernel function of a target dataset.

Claims20

Dependent claims

A system, comprising:

a processor that executes computer-executable components stored in a non-transitory computer-readable memory, the computer-executable components comprising:
a computation component that calculates, on logical or physical qubits of a quantum system, a plurality of kernels for subsets of features of a feature map and centers the plurality of kernels within a feature space of the feature map;
a regularization component that regularizes parameters to combine the plurality of kernels;
and
an integration component that combines the plurality of kernels into a combined kernel.

2 more elements

Dependencies are inferred from the claim text.

Training a combination of multiple quantum and classical kernels

Life of this patent

Training a combination of multiple quantum and classical kernels

Life of this patent

Abstract

Claims20