International business machines corporation (20240135226). QUANTUM COMPUTING BASED KERNEL ALIGNMENT FOR A SUPPORT VECTOR MACHINE TASK simplified abstract

QUANTUM COMPUTING BASED KERNEL ALIGNMENT FOR A SUPPORT VECTOR MACHINE TASK

Organization Name

international business machines corporation

Inventor(s)

Gian Gentinetta of Chavannes-près-Renens (CH)

David Sutter of Zuerich (CH)

Stefan Woerner of Zurich (CH)

QUANTUM COMPUTING BASED KERNEL ALIGNMENT FOR A SUPPORT VECTOR MACHINE TASK - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240135226 titled 'QUANTUM COMPUTING BASED KERNEL ALIGNMENT FOR A SUPPORT VECTOR MACHINE TASK

Simplified Explanation

The patent application describes techniques for optimizing a quantum kernel for a support vector machine task.

• The digital processor receives a set of training data, each representing a data vector (x) and a label (y) identifying the respective member to be part of either a first class or a second class.
• The digital processor provides the quantum kernel comprising a set of unitary operations adapted for acting on a zero state of qubits of a universal quantum circuit.
• The quantum processor performs an alignment of the quantum kernel using an optimization algorithm based on the set of training data on a primal problem approach of the support vector machine task.

1. 1. 1. Potential Applications

- Quantum computing - Machine learning - Support vector machines

1. 1. 1. Problems Solved

- Optimization of quantum kernels for support vector machine tasks - Efficient processing of large datasets in quantum computing

1. 1. 1. Benefits

- Improved performance of support vector machines - Enhanced efficiency in quantum computing tasks

1. 1. 1. Potential Commercial Applications
         1. Quantum Kernel Optimization for Support Vector Machines

- Finance industry for risk analysis - Healthcare industry for medical diagnosis - Cybersecurity for threat detection

1. 1. 1. Possible Prior Art

There may be prior art related to quantum computing algorithms for machine learning tasks, but specific examples are not provided in the abstract.

1. 1. 1. 1. Unanswered Questions
         2. How does the optimization algorithm improve the performance of the quantum kernel?

The abstract mentions the use of an optimization algorithm for aligning the quantum kernel, but it does not detail how this process specifically enhances the performance of the quantum kernel.

1. 1. 1. 1. What are the limitations of using quantum kernels for support vector machine tasks?

While the abstract describes techniques for optimizing a quantum kernel for support vector machines, it does not address any potential limitations or challenges that may arise when implementing this technology.

Original Abstract Submitted

described are techniques for optimizing a quantum kernel for a support vector machine task. the techniques include receiving, by digital processor, a set of training data, each member of the set representing a data vector (x) and a label (y) identifying the respective member to be part of either a first class or a second class the techniques further include providing, by the digital processor, the quantum kernel comprising a set of unitary operations adapted for acting on a zero state of qubits of a universal quantum circuit the techniques further include performing, by a quantum processor comprising a set of interlinked quantum circuits, an alignment of the quantum kernel using an optimization algorithm based on the set of training data on a primal problem approach of the support vector machine task.