View source for 18047397. QUANTUM COMPUTING BASED KERNEL ALIGNMENT FOR A SUPPORT VECTOR MACHINE TASK simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

=QUANTUM COMPUTING BASED KERNEL ALIGNMENT FOR A SUPPORT VECTOR MACHINE TASK=


==Organization Name==

[[:Category:INTERNATIONAL BUSINESS MACHINES CORPORATION|INTERNATIONAL BUSINESS MACHINES CORPORATION]]


[[Category:INTERNATIONAL BUSINESS MACHINES CORPORATION]]

==Inventor(s)==

[[:Category:Gian Gentinetta of Chavannes-près-Renens (CH)|Gian Gentinetta of Chavannes-près-Renens (CH)]][[Category:Gian Gentinetta of Chavannes-près-Renens (CH)]]

[[:Category:David Sutter of Zuerich (CH)|David Sutter of Zuerich (CH)]][[Category:David Sutter of Zuerich (CH)]]

[[:Category:Stefan Woerner of Zurich (CH)|Stefan Woerner of Zurich (CH)]][[Category: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 18047397 titled 'QUANTUM COMPUTING BASED KERNEL ALIGNMENT FOR A SUPPORT VECTOR MACHINE TASK

==Simplified Explanation==

The abstract describes techniques for optimizing a quantum kernel for a support vector machine task. This involves receiving a set of training data, providing a quantum kernel with unitary operations, and performing an alignment of the quantum kernel using an optimization algorithm based on the training data.

* Quantum kernel optimization techniques for support vector machines:
    - Receive training data representing data vectors and labels
    - Provide quantum kernel with unitary operations for qubits
    - Perform alignment using optimization algorithm on quantum processor

== Potential Applications ==
The technology can be applied in various fields such as machine learning, quantum computing, and data analysis.

== Problems Solved ==
This technology addresses the challenge of optimizing quantum kernels for support vector machine tasks, improving efficiency and accuracy in processing large datasets.

== Benefits ==
The benefits of this technology include enhanced performance, increased speed, and improved accuracy in classification tasks.

== Potential Commercial Applications ==
Potential commercial applications include quantum computing services, machine learning software, and data analysis tools.

== Possible Prior Art ==
One possible prior art in this field is the use of classical support vector machines for classification tasks, which do not leverage quantum computing techniques for optimization.

== Unanswered Questions ==

=== How does the quantum kernel optimization technique compare to traditional methods in terms of performance and accuracy? ===
The article does not provide a direct comparison between the quantum kernel optimization technique and traditional methods in terms of performance and accuracy.

=== What are the limitations or challenges of implementing this quantum kernel optimization technique in practical applications? ===
The article does not discuss the limitations or challenges of implementing this quantum kernel optimization technique in practical applications.


==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.



[[Category:G06N10/60]]