20240012786. CLASSICAL-QUANTUM DATA CONFIDENCE FABRIC simplified abstract (Dell Products L.P.)

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CLASSICAL-QUANTUM DATA CONFIDENCE FABRIC

Organization Name

Dell Products L.P.

Inventor(s)

Kenneth Durazzo of Morgan Hill CA (US)

Stephen J. Todd of North Andover MA (US)

Michael Robillard of Shrewsbury MA (US)

Victor Fong of Melrose MA (US)

CLASSICAL-QUANTUM DATA CONFIDENCE FABRIC - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240012786 titled 'CLASSICAL-QUANTUM DATA CONFIDENCE FABRIC

Simplified Explanation

The abstract of the patent application describes a method implemented by a hybrid classical-quantum computing system that receives data from a node of a data confidence fabric. The system processes the data to create processed data and generates confidence scores related to the processed data. The confidence scores and processed data are made available to an end user. The hybrid computing system can also function as a node of the data confidence fabric and perform classical and/or quantum computing operations on the data.

  • The method involves receiving data from a node of a data confidence fabric.
  • The received data is processed to create processed data.
  • Confidence scores are generated based on the processed data.
  • The confidence scores and processed data are made accessible to an end user.
  • The hybrid classical-quantum computing system can also act as a node of the data confidence fabric.
  • The system can perform classical and/or quantum computing operations on the data.

Potential Applications:

  • Data analysis and processing in various industries such as finance, healthcare, and manufacturing.
  • Quantum computing applications that require confidence scoring and data processing.

Problems Solved:

  • Efficient processing and analysis of data from a data confidence fabric.
  • Integration of classical and quantum computing capabilities for improved data processing.

Benefits:

  • Enhanced data processing capabilities through the use of hybrid classical-quantum computing.
  • Improved confidence scoring for processed data.
  • Potential for solving complex problems that require both classical and quantum computing.


Original Abstract Submitted

one example method includes receiving, by a hybrid classical-quantum computing system, data from a node of a data confidence fabric, processing the data to create processed data, generating one or more confidence scores relating to the processed data, and making the one or more confidence scores and the processed data available to an end user. the hybrid classical-quantum computing system may also be a node of the data confidence fabric and may perform classical and/or quantum computing operations on the data.

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