Quantum Technique Recommendation for Automated Hardware Benchmarking
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Solution Overview
Problem
Existing approaches fail to automatically identify and benchmark optimal techniques comprising quantum computing features for solving a variety of computational problems, lacking an intelligent platform to assist users in selecting suitable quantum algorithms, procedures, and hardware for specific tasks.
Innovation Solution
An intelligent and automated system that employs a machine learning model to recommend combinations of quantum circuits, algorithms, quantum hardware units, error mitigation techniques, and procedures to solve defined problems, trained using a training set of previous executions and feedback, and capable of executing these combinations on a quantum computing platform to analyze results for optimal solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing approaches implement AI in connection with classical computing to identify techniques, then automation of technique identification is improved, but the ability to automatically identify and benchmark optimal techniques comprising quantum computing features deteriorates
Solution Approach 1:
The machine learning model is designed to handle multiple types of computational problems (optimization, classification, simulation) and automatically identify appropriate quantum computing techniques for each, making the system universally applicable across different domains while maintaining specialized quantum computing capabilities
Solution Approach 2:
The patent introduces a hybrid system that acts as an intermediary between classical AI and quantum computing, using classical machine learning models to identify and benchmark quantum techniques, thereby bridging the gap between classical automation and quantum capability identification
2Measurement precision
If manual identification of quantum computing techniques is used, then precision in selecting optimal techniques is improved, but productivity and automation deteriorate
Solution Approach 1:
The system implements feedback loops where the machine learning model continuously learns from benchmarking results and execution outcomes, improving its precision in technique selection over time while maintaining high productivity through automated iteration and refinement of recommendations
Solution Approach 2:
The patent employs preliminary benchmarking and training phases where the machine learning model is pre-trained on datasets of quantum computing techniques and their performance characteristics, enabling it to make accurate recommendations without requiring manual analysis during actual problem-solving
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to an intelligent and automated system to solve quantum computing related problems. The computer-implemented system can comprise a memory that can store computer-executable components. The computer-implemented system can further comprise a processor that can execute the computer-executable components stored in the memory, wherein the computer-executable components can comprise a recommendation component that can employ a machine learning model to generate, based on an input, a recommendation comprising a combination of entities comprising, one or more quantum circuits, one or more algorithms, one or more quantum hardware units, one or more error mitigation or error correction techniques, and one or more quantum procedures, to solve a defined problem comprised in the input.