Quantum Circuit Simulation via Comparative Rejection Sampling
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Solution Overview
Problem
Current quantum computing benchmarking techniques are inefficient and often obsolete due to frequent changes in quantum computer calibration, leading to prolonged simulation times and potential errors in selecting suitable hardware providers for executing quantum circuits.
Innovation Solution
A comparative rejection sampling technique is employed to simulate and compare execution run results of quantum circuits across multiple hardware providers, determining error rates and optimizing the number of samples evaluated to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional quantum computing benchmarking techniques are used, then comprehensive error rate assessment can be obtained, but simulation time becomes excessively long and computational resources are wasted
Solution Approach 1:
The patent applies partial action by evaluating only a selected subset of possible execution run results rather than all possible results. By identifying and evaluating only those samples that are most informative for determining error rates, the system achieves adequate benchmarking accuracy with significantly reduced simulation time and computational resources.
Solution Approach 2:
The patent segments the evaluation process into distinct phases: first identifying a comprehensive set of possible execution run results, then selecting a subset of informative samples, and finally evaluating only those selected samples. This segmentation allows the system to avoid the computational burden of evaluating all possible results while maintaining assessment accuracy.
2Measurement precision
If all possible execution run results are evaluated to ensure accurate benchmarking, then measurement precision is improved, but device complexity and computational overhead increase
Solution Approach 1:
The system performs partial evaluation by focusing computational resources on a strategically selected subset of execution run results. By using criteria to identify which samples provide the most information about error rates, the system achieves accurate benchmarking without the excessive computational complexity of evaluating all possible results.
Solution Approach 2:
The patent extracts only the most informative samples from the complete set of possible execution run results. By applying selection criteria to identify and extract these key samples, the system reduces computational complexity while preserving the essential information needed for accurate error rate assessment.
3Adaptability or versatility
If quantum computer calibration changes frequently, then hardware performance varies, but current benchmarking techniques become obsolete and require prolonged re-simulation
Solution Approach 1:
The patent enables rapid re-benchmarking by evaluating only a selected subset of execution run results when hardware calibration changes. This partial evaluation approach allows the system to quickly assess whether hardware providers still meet performance requirements without performing complete re-simulations, thus maintaining adaptability while reducing time loss.
4Measurement precision
If the number of samples evaluated is increased to improve statistical accuracy, then measurement precision improves, but productivity and efficiency decrease
Solution Approach 1:
The system achieves adequate statistical accuracy by evaluating a carefully selected subset of samples rather than increasing the total number of samples evaluated. By focusing computational resources on the most informative samples, the system maintains measurement precision while preserving benchmarking throughput and productivity.
Data Source
AI summary
A comparative rejection sampling technique selects a bound set of possible execution run results for a process to be simulated, such as a quantum circuit, for each possible execution run result a modeled probability of a state associated with the possible execution run result is determined. For example a tensor network algorithm may be used to determine quantum state probabilities for each quantum state execution result included in the bound set. Based on the determined probabilities one of the possible execution run results is selected from the set of possible execution run results as an accepted simulated execution run result for a run of the process being simulated.