Quantum Processor Error Modeling with Iterative Benchmark Learning
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Solution Overview
Problem
Direct measurement of performance metrics for quantum algorithms on quantum computing systems is computationally intractable due to the significant resources and time required, making it impractical to calibrate O(N) qubits for training a machine-learned model, where N represents the number of qubits.
Innovation Solution
An iterative supervised learning approach is employed to construct an error model using O(1) qubit gate benchmarks, progressively increasing complexity through constrained training iterations, allowing estimation of O(N) qubit quantum algorithm metrics from simpler gate benchmarks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct measurement of performance metrics for quantum algorithms is performed on quantum computing systems, then measurement precision is improved, but computational time and resources required increase significantly
Solution Approach 1:
The patent creates a machine-learned model that copies and predicts performance metrics for O(N) qubit quantum algorithms by training on simplified O(1) qubit gate benchmarks. Instead of directly measuring complex quantum algorithms, the model learns from simpler subsystem data and generalizes to predict overall algorithm performance, thereby avoiding the computationally intractable direct measurement process.
Solution Approach 2:
The patent segments the complex quantum algorithm performance measurement problem into smaller, manageable parts by using O(1) qubit gate benchmarks as training data. The error model is trained on simplified gate-level error indicators from a subset of qubits, then used to predict performance metrics for the full O(N) qubit system, breaking down the intractable measurement task into tractable segments.
2Measurement precision
If calibration of O(N) qubits is performed for training machine-learned models, then measurement precision is improved, but device complexity and time requirements increase
Solution Approach 1:
The patent uses simplified O(1) qubit gate benchmarks as proxy copies to represent the complex O(N) qubit system. Instead of calibrating all N qubits directly, the model trains on calibrated error indicators from a small subset of qubits and uses these simplified representations to predict performance metrics for the full quantum algorithm, reducing calibration complexity while maintaining predictive accuracy.
Solution Approach 2:
The patent segments the calibration process by focusing only on a small subset of O(1) qubits for training purposes, rather than attempting to calibrate all O(N) qubits. The error model is trained on segmented data from individual gates and qubits, then assembled to predict overall system performance, significantly reducing the complexity and time required for calibration.
3Measurement precision
If training data for quantum algorithm performance models is collected through direct measurement, then model accuracy is improved, but computational resources and time required become intractable
Solution Approach 1:
The patent collects training data by measuring simplified O(1) qubit gate benchmarks rather than directly measuring O(N) quantum algorithm performance. These simplified measurements serve as proxy data that captures essential error characteristics, allowing the model to learn accurate performance predictions without requiring computationally intensive direct measurements of the full quantum algorithm.
Solution Approach 2:
The patent segments the data collection process by gathering error indicators from a small subset of O(1) qubits and gates, rather than attempting to measure all O(N) qubits simultaneously. This segmented approach to data collection produces sufficient training data for accurate model training while being computationally tractable and efficient.
Data Source
AI summary
Systems and methods for generating error models for quantum algorithms implemented on quantum processors having a plurality of qubits are provided. In one example, a method includes obtaining data associated with a benchmark model, the benchmark model having one or more error indicators as features, one or more benchmarks as targets, and one or more trainable parameters, wherein each error indicator is associated with a distinct quantum gate calibrated in a distinct operating configuration associated with a plurality of operating parameters for the quantum gate and associated with a calibration data for the operating configuration. The method includes determining parameter values for the trainable parameters. The method include operating a quantum computing system based on operating parameters determined based on the parameter values.