18345715. SELF-LEARNING QUANTUM COMPUTING PLATFORM simplified abstract (Dell Products L.P.)
Contents
- 1 SELF-LEARNING QUANTUM COMPUTING PLATFORM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SELF-LEARNING QUANTUM COMPUTING PLATFORM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SELF-LEARNING QUANTUM COMPUTING PLATFORM
Organization Name
Inventor(s)
Victor Fong of Medford MA (US)
Brendan Burns Healy of Haddonfield NJ (US)
Benjamin E. Santaus of Somerville MA (US)
SELF-LEARNING QUANTUM COMPUTING PLATFORM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18345715 titled 'SELF-LEARNING QUANTUM COMPUTING PLATFORM
Simplified Explanation
The method described in the patent application involves using a machine learning model to predict runtime characteristics and resource requirements for a quantum computing function, selecting an execution environment, and executing the function.
- Predicting runtime characteristics and resource needs for a quantum computing function using a machine learning model.
- Selecting an execution environment for the quantum computing function based on the predictions.
- Executing the quantum computing function in the chosen environment.
Potential Applications
This technology could be applied in various industries such as finance, healthcare, and logistics for optimizing quantum computing tasks.
Problems Solved
This method helps in efficiently managing resources and selecting the most suitable execution environment for quantum computing functions, improving overall performance.
Benefits
The technology improves the efficiency and accuracy of quantum computing tasks, leading to faster and more reliable results.
Potential Commercial Applications
"Optimizing Quantum Computing Functions with Machine Learning" - This technology could be utilized by quantum computing service providers to offer enhanced performance and resource management to their clients.
Possible Prior Art
There may be prior art related to optimizing quantum computing tasks using machine learning models, but specific examples are not provided in this article.
Unanswered Questions
How does this method handle unexpected changes in runtime characteristics or resource requirements during execution?
The article does not address how the method adapts to unforeseen variations in runtime characteristics or resource needs once the quantum computing function is in progress.
What types of machine learning models are most effective for predicting quantum computing function characteristics and resource requirements?
The article does not specify which machine learning models are best suited for accurately predicting the runtime characteristics and resource needs of quantum computing functions.
Original Abstract Submitted
A method includes predicting, using a machine learning model, runtime characteristics concerning a quantum computing function, predicting, using the machine learning model, resources needed to perform the quantum computing function, selecting an execution environment for the quantum computing function, and executing the quantum computing function in the execution environment. The quantum computing function may be a quantum circuit cutting operation, or the quantum computing function may be a quantum circuit execution.
