Quantum Scalability Modeling for Large-Scale Performance Prediction
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Solution Overview
Problem
Evaluating the scalability of quantum computing systems is challenging due to increasing sources of degradation and control complexity as systems scale, making it infeasible to extrapolate performance from smaller to larger systems, and costly to construct large-scale systems for measurement.
Innovation Solution
Develop systems and methods to evaluate scalability using benchmark performance data from smaller-scale quantum systems, applying scalability models to predict performance at larger scales, including hardware architectures and control strategies, and simulate or construct smaller-scale systems to extrapolate performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large-scale quantum systems are constructed for direct measurement, then measurement accuracy improves, but construction cost and resource requirements worsen
Solution Approach 1:
The patent creates virtual copies of quantum systems through classical simulation. Instead of constructing physical large-scale quantum systems, the invention simulates their behavior using classical computers, thereby obtaining performance measurements without the exponential resource requirements of actual quantum hardware.
Solution Approach 2:
The patent introduces an intermediary classical simulation layer between the researcher and the quantum system. This intermediary allows indirect measurement of quantum system performance through classical computation, avoiding the need to directly construct and measure large-scale quantum systems.
2Power
If quantum systems are scaled up to improve computational capability, then processing power improves, but control complexity worsens
Solution Approach 1:
The patent creates virtual models of scaled-up quantum systems through classical simulation. These virtual copies allow researchers to study control strategies and system behavior at large scales without the exponential increase in actual control hardware and operational complexity.
Solution Approach 2:
The patent performs preliminary classical simulations to optimize control strategies before implementing them on actual quantum hardware. This preliminary action allows control parameters to be tuned and validated in silico, reducing the complexity of actual system control.
3Power
If quantum systems are scaled up to improve computational capability, then processing power improves, but degradation sources worsen
Solution Approach 1:
The patent creates virtual representations of scaled quantum systems that incorporate models of degradation sources. These simulations allow researchers to study and mitigate the effects of degradation without the compounding problems that arise in actual large-scale quantum systems.
Data Source
AI summary
Systems and methods for evaluating the scalability of quantum systems are provided. In one example, a method may include obtaining benchmark performance data for a candidate quantum system architecture. The benchmark performance data may be descriptive of one or more performance characteristics of the candidate quantum system architecture for a plurality of processor sizes. The method may include obtaining one or more scaling parameters based on the benchmark performance data, including a quantum scaling model relating processor size of the candidate quantum system architecture to the one or more performance characteristics. The method may include determining one or more scaling metrics for the candidate quantum system architecture at a scaled processor size greater than the plurality of processor sizes by the quantum scaling model. The method may include determining one or more control actions for an operational quantum system based on the one or more scaling metrics.