Quantum Program Packing for Cloud Selection
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Solution Overview
Problem
Selecting an optimal quantum computer for executing tasks across different cloud platforms is challenging due to varying hardware properties and performance parameters, which affects execution results and resource utilization, necessitating a method to simplify the selection process and optimize performance.
Innovation Solution
A method involving a filtering scheme to evaluate and select quantum computers based on performance parameters, using both static and dynamic analyses to assess execution pairs, and packing quantum programs into joint circuits that optimize resource utilization and minimize non-effective volume, while considering quantum restrictions and auxiliary qubit management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum programs are executed multiple times on different quantum computers, then execution results and performance parameters can be evaluated, but the complexity of selecting the optimal quantum computer increases
Solution Approach 1:
The patent segments the quantum computer selection process into distinct phases: (1) filtering quantum computers based on hardware properties and task requirements, (2) evaluating performance parameters through static and dynamic analyses of execution pairs, and (3) selecting the optimal quantum computer based on evaluated metrics. This segmentation reduces the complexity of the overall selection process while maintaining evaluation accuracy.
2Measurement precision
If quantum programs are executed multiple times to gather performance data, then selection accuracy improves, but execution time and resource consumption increase
Solution Approach 1:
The patent performs preliminary filtering of quantum computers based on hardware properties and task requirements before executing performance evaluations. This preliminary action reduces the number of quantum computers that need to be evaluated through multiple executions, thereby reducing the total execution time while maintaining selection accuracy.
Solution Approach 2:
The patent evaluates quantum computers for a limited number of times (two or more executions) rather than exhaustive testing. This partial action provides sufficient performance data to make accurate selections while avoiding the excessive time consumption that would result from more extensive testing.
3Productivity
If quantum computers with more qubits are selected, then task execution capability improves, but resource utilization efficiency may decrease
Solution Approach 1:
The patent changes the evaluation parameters from simply counting qubits to assessing effective resource utilization. The performance evaluation metrics focus on how efficiently quantum computers use their available qubits and other resources to complete tasks, rather than merely having more resources available. This parameter change enables selection of quantum computers that optimize both capability and efficiency.
Data Source
AI summary
A method, product and apparatus comprising: obtaining an indication of an execution task to be performed by a quantum computer, wherein the execution task comprises executing, by the quantum computer, a quantum program for a number of times that is larger than two times: obtaining a graph comprising nodes that are connected by edges, the graph represents a gate-level implementation of the quantum program, the graph depicts quantum restrictions of the quantum program; and packing multiple graphs according to the quantum restrictions to synthesize a joint circuit, the joint circuit is configured, when executed by the quantum computer, to implement the execution task, the multiple graphs comprise at least one instance of the graph, the one instance of the graph represents a single execution of the quantum program, whereby execution of the joint circuit implements execution of the quantum program for the number of times.