View source for 17540485. OPTIMIZED SELECTION OF DATA FOR QUANTUM CIRCUITS simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

=OPTIMIZED SELECTION OF DATA FOR QUANTUM CIRCUITS=


==Organization Name==

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


[[Category:INTERNATIONAL BUSINESS MACHINES CORPORATION]]

==Inventor(s)==

[[:Category:Frederik Frank Fl�ther of Schlieren (CH)|Frederik Frank Fl�ther of Schlieren (CH)]][[Category:Frederik Frank Fl�ther of Schlieren (CH)]]

[[:Category:Michele Grossi of Prevessin-Möens (FR)|Michele Grossi of Prevessin-Möens (FR)]][[Category:Michele Grossi of Prevessin-Möens (FR)]]

[[:Category:Vaibhaw Kumar of Frederick MD (US)|Vaibhaw Kumar of Frederick MD (US)]][[Category:Vaibhaw Kumar of Frederick MD (US)]]

[[:Category:Robert E. Loredo of North Miami Beach FL (US)|Robert E. Loredo of North Miami Beach FL (US)]][[Category:Robert E. Loredo of North Miami Beach FL (US)]]

==OPTIMIZED SELECTION OF DATA FOR QUANTUM CIRCUITS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17540485 titled 'OPTIMIZED SELECTION OF DATA FOR QUANTUM CIRCUITS

==Simplified Explanation==

The patent application describes a data selection system for quantum computers that selects a suitable subset of data to be processed by a quantum algorithm.

* The system compresses and clusters a data set based on a specific distribution criteria.
* It selects a subset of clustered data that represents the overall data set.
* The selected subset is processed by a quantum device.
* The system generates a metric score based on the performance of the quantum algorithm.
* The selected subset is refined over multiple iterations based on successive metric scores.
* The process continues until a termination criterion is met.
* The final selected subset is used as input for the quantum computer to execute the processing task.

== Potential Applications ==
* Quantum computing tasks that require processing large data sets.
* Optimization problems that can benefit from quantum algorithms.
* Machine learning tasks that involve large-scale data analysis.

== Problems Solved ==
* Overcoming the limitations of input data size for quantum computers.
* Selecting a representative subset of data for quantum processing.
* Improving the efficiency and performance of quantum algorithms.

== Benefits ==
* Enables meaningful computational results with limited input data for quantum computers.
* Reduces the computational complexity by selecting a subset of data.
* Improves the overall efficiency and accuracy of quantum algorithms.


==Original Abstract Submitted==

To obtain meaningful computational results despite limits on the amount of data that can be input to a quantum computer, a data selection system uses an iterative approach to select a suitable subset of data to be input to a quantum device for processing by a quantum algorithm. The system compresses and clusters a data set according to a task-specific distribution criteria and selects a subset of this clustered data corresponding to representative cases of the data. The selected subset is processed by the quantum device and the system generates a metric score based on the degree to which the results satisfy a performance criterion. The selected subset is refined over multiple iterations based on successive metric scores until a termination criterion is reached, and the final selected subset of data is used as input to the quantum computer for execution of the processing task.



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