US 2026/0099752 A1 · SYSTEMS AND METHODS FOR QUANTUM CIRCUIT SIMULATION USING TENSOR NETWORKS

Abstract

Embodiments of the present disclosure provide functionality to tensor network framework designed for quantum kernel methods and demonstration of tensor network effectiveness at scaling this application. Quantum kernels capture the distance between data points in quantum feature space by evaluating the quantum state overlaps associated with each data point. It has been found that expressing data in quantum feature space may produce more separable data that improves the results of linear classifiers. The different kernel elements may be computed independently, and parallel processing may be exploited to significantly reduce computational time, enabling to train on more data. Thus, quantum kernels continue to improve classification metrics with the addition of more training data and more features.

Claims20

Dependent claims

A system for quantum kernel enhanced training of a target machine learning model data architecture using a quantum feature map applied to training data set, the system comprising:

a plurality of computer processors, operating in conjunction with computer memory and non-transitory computer readable storage media, at least one processor of the plurality of processors configured to:
receive a corresponding data batch of the training data set;
receive a parameterized quantum circuit;
transform each data point in the data batch using the parameterized quantum circuit as the quantum feature map to convert the data point into a quantum-space data point having qubits based on a manifold space defined by the parameterized quantum circuit, each quantum-space data point having states represented using a tensor network;
generate a quantum kernel representing a quantum computing enhanced training data set wherein each data point of the training data set is expanded based on the expectation values of the qubits for capturing a distance between each data point in the manifold space by simulating quantum state overlaps associated with each data point;
and
train the target machine learning model data architecture using the quantum computing enhanced training data set to generate an output trained target machine learning model data architecture.

4 more elements

A method for quantum kernel enhanced training of a target machine learning model data architecture using a quantum feature map applied to training data set, the method comprising:

receiving a corresponding data batch of the training data set;
receiving a parameterized quantum circuit;
transforming each data point in the data batch using the parameterized quantum circuit as the quantum feature map to convert the data point into a quantum-space data point having qubits based on a manifold space defined by the parameterized quantum circuit, each quantum-space data point having states represented using a tensor network;
generating a quantum kernel representing a quantum computing enhanced training data set wherein each data point of the training data set is expanded based on the expectation values of the qubits for capturing a distance between each data point in the manifold space by simulating quantum state overlaps associated with each data point;
and
training the target machine learning model data architecture using the quantum computing enhanced training data set to generate an output trained target machine learning model data architecture.

3 more elements

Dependencies are inferred from the claim text.

Systems and methods for quantum circuit simulation using tensor networks

Life of this patent

Systems and methods for quantum circuit simulation using tensor networks

Life of this patent

Abstract

Claims20