17894640. QUANTUM VARIATIONAL NETWORK CLASSIFIER simplified abstract (International Business Machines Corporation)

QUANTUM VARIATIONAL NETWORK CLASSIFIER

Organization Name

International Business Machines Corporation

Inventor(s)

Jae-Eun Park of Wappingers Falls NY (US)

Abhijit Mitra of The Woodlands TX (US)

Vladimir Rastunkov of Mundelein IL (US)

Vaibhaw Kumar of Frederick MD (US)

Dimitrios Alevras of West Chester PA (US)

QUANTUM VARIATIONAL NETWORK CLASSIFIER - A simplified explanation of the abstract

This abstract first appeared for US patent application 17894640 titled 'QUANTUM VARIATIONAL NETWORK CLASSIFIER

Simplified Explanation

- A processor controls quantum hardware to transform qubit states associated with pairs of data points in a training dataset using a circuit parameter representing a rotation angle. - Inner products of transformed qubit states for the pairs of data points are computed. - The processor minimizes an objective function based on the inner products to find a target circuit parameter that minimizes the function. - A kernel matrix is built based on the inner products computed for a sample dataset and the target circuit parameter. - A classification algorithm uses the kernel matrix to classify the sample dataset.

Potential Applications

- Quantum machine learning - Data classification and pattern recognition

Problems Solved

- Efficient processing of large datasets - Improved accuracy in classification tasks

Benefits

- Utilizes quantum hardware for faster computation - Enhances classification accuracy - Enables complex data analysis and pattern recognition

Original Abstract Submitted

A processor can control quantum hardware to transform qubit states associated with a plurality of pairs of data points in a training dataset using a circuit parameter representing a rotation angle. Inner products of transformed qubit states associated with the plurality of pairs of data points can be computed. The processor can minimize an objective function based on the inner products, where the minimizing finds a target circuit parameter representing a target rotation angle that minimizes the objective function. A processor can build a kernel matrix based on the inner products computed for a sample dataset and the target circuit parameter passed to the quantum hardware. A classification algorithm can use the kernel matrix to classify the sample dataset.