INTERNATIONAL BUSINESS MACHINES CORPORATION (20240320536). HANDLING BLACK SWAN EVENTS ON QUANTUM COMPUTERS simplified abstract

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HANDLING BLACK SWAN EVENTS ON QUANTUM COMPUTERS

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Arkadiy O. Tsfasman of Wappingers Falls NY (US)

Vladimir Rastunkov of Mundelein IL (US)

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

John S. Werner of Fishkill NY (US)

HANDLING BLACK SWAN EVENTS ON QUANTUM COMPUTERS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240320536 titled 'HANDLING BLACK SWAN EVENTS ON QUANTUM COMPUTERS

Simplified Explanation:

This patent application describes a method, system, and computer program product for detecting and handling black swan events on a quantum computing device.

  • Sensor data from the environment of the quantum computing device is captured and compared to historical sensor data to detect black swan events.
  • A black swan event is identified if the difference between the captured sensor data and historical data exceeds a threshold value.
  • When a black swan event is detected, a machine learning model is executed to determine the appropriate action to take.
  • The machine learning model identifies the action by matching the captured sensor data to a neuron of a self-organizing map and finding the closest cluster of data within that neuron.

Key Features and Innovation:

  • Detection and handling of black swan events on a quantum computing device.
  • Utilization of machine learning models to identify actions in response to black swan events.
  • Matching captured sensor data to a self-organizing map to determine the appropriate action.

Potential Applications:

This technology can be applied in various industries where the detection and handling of unexpected events are critical, such as finance, healthcare, and cybersecurity.

Problems Solved:

This technology addresses the challenge of identifying and responding to rare and unpredictable events that can impact the performance of a quantum computing device.

Benefits:

  • Improved resilience and performance of quantum computing devices.
  • Enhanced ability to respond to unforeseen events in real-time.
  • Increased reliability and efficiency of quantum computing systems.

Commercial Applications:

Potential commercial applications include risk management systems, anomaly detection tools, and predictive maintenance solutions for quantum computing devices.

Prior Art:

Readers interested in prior art related to this technology can explore research on machine learning models for anomaly detection and self-organizing maps in the context of quantum computing.

Frequently Updated Research:

Stay updated on advancements in machine learning algorithms for anomaly detection and self-organizing maps in quantum computing environments.

Questions about Quantum Computing Black Swan Event Handling: 1. How does this technology improve the resilience of quantum computing devices in handling unexpected events? 2. What are the key advantages of using machine learning models to identify actions in response to black swan events on quantum computing devices?


Original Abstract Submitted

a method, system, and computer program product for handling black swan events on a quantum computing device. sensor data from an environment of the quantum computing device is captured and compared to historical sensor data of the environment of the quantum computing device. a black swan event is detected if the difference between the captured sensor data and the historical sensor data exceeds a threshold value. upon detecting a black swan event, such as during the time that the quantum processor is being utilized, a machine learning model is executed to identify the action to be performed to handle the black swan event. the machine learning model identifies such an action based on identifying a neuron of a self-organizing map that most closely matches the captured sensor data, and then identifying which of the clusters of data within the identified neuron is closest to the captured sensor data.

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