Quantum Computer Selection Using Staged Performance Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Selecting an optimal quantum computer for executing a quantum program is challenging due to varying hardware properties and performance parameters across different quantum computers, which can affect compilation, error rates, and execution time, necessitating a simplified and user-friendly method for identifying the most effective quantum computers for an execution task.
Innovation Solution
A filtering scheme is employed to select quantum computers based on performance parameters, using both static and dynamic analyses to estimate and compare these parameters across multiple quantum computers, and an objective function to determine the optimal computer for a specific task, while considering user preferences and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum computers are selected based on detailed performance parameter analysis, then selection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the quantum computer selection process into distinct phases: initial filtering based on hardware constraints, static analysis of performance parameters, and dynamic analysis for final selection. This segmentation allows complex multi-criteria evaluation to be broken down into manageable steps, improving selection accuracy without overwhelming system complexity
Solution Approach 2:
The patent performs preliminary filtering of quantum computers based on hardware constraints before conducting detailed performance analysis. By pre-filtering candidates that meet minimum requirements, the system reduces the scope of subsequent complex analyses, thereby improving overall selection accuracy while controlling system complexity
2Loss of time
If static analysis is used to estimate performance parameters, then analysis time is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent applies static analysis as a preliminary step to quickly estimate performance parameters and identify promising quantum computers. These estimates serve as initial filtering criteria, allowing the system to rapidly eliminate unsuitable candidates before applying more time-consuming dynamic analysis methods to refine the selection
Solution Approach 2:
The patent segments the performance analysis into static and dynamic components, using static analysis for initial evaluation and dynamic analysis for final verification. This segmentation allows the system to balance analysis time and precision by applying appropriate methods at different stages of the selection process
3Measurement precision
If dynamic analysis is performed to obtain accurate performance data, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent performs dynamic analysis on only a subset of quantum computers that passed the static analysis filter, rather than conducting exhaustive dynamic analysis on all available systems. This partial application of dynamic analysis maintains measurement precision for the final selection while preserving productivity by avoiding unnecessary analysis of already-filtered candidates
4Measurement precision
If multiple quantum computers are evaluated in detail, then selection quality is improved, but loss of time increases
Solution Approach 1:
The patent segments the evaluation process into hierarchical stages with increasing depth of analysis. Quantum computers are evaluated through successive filters (hardware constraints, static analysis, dynamic analysis), with only those passing each stage subjected to more detailed evaluation. This segmentation maintains selection quality by thoroughly evaluating promising candidates while reducing total evaluation time through early elimination of unsuitable systems
Solution Approach 2:
The patent performs preliminary evaluations using faster methods (hardware constraint checking, static analysis) before conducting detailed evaluations. This preliminary action identifies and eliminates unsuitable quantum computers early in the process, allowing detailed evaluation to be focused only on the most promising candidates, thereby improving selection quality without proportionally increasing total evaluation time
Data Source
AI summary
A method, apparatus, and product comprising: obtaining an indication of an execution task, the execution task comprises executing a quantum program a number of times, the number of times is larger than two times; and selecting a quantum computer from a set of two or more quantum computers for performing the execution task, the set of two or more quantum computers comprise a first quantum computer and a second quantum computer, said selecting the quantum computer is performed based on a first value of a performance parameter that is associated with the first quantum computer and based on a second value of the performance parameter that is associated with the second quantum computer.