Refining Qubit Calibration Models with Supervised Learning
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Solution Overview
Problem
Existing qubit calibration models are often too simple and fail to accurately capture the complex parameters and imperfections in qubit calibration data, leading to unreliable extraction of qubit parameters.
Innovation Solution
The use of supervised machine learning algorithms to refine qubit calibration models by learning perturbations from calibration data, allowing for the capture of features not accounted for in the original models, such as higher-order effects and unforeseen physics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple qubit calibration models are used, then the calibration process is easier and faster, but the accuracy and reliability of parameter extraction deteriorates
Solution Approach 1:
The patent combines simple parametric models with machine learning models to create a hybrid calibration model. The parametric model provides physical interpretability and fast computation, while the machine learning component captures complex nonlinear relationships and imperfections in the data, achieving both speed and accuracy in parameter extraction
Solution Approach 2:
The calibration model is divided into multiple components: a base parametric model for capturing primary physical relationships and separate machine learning models for correcting systematic errors and capturing higher-order effects. This segmentation allows each component to specialize in specific aspects of the calibration problem
2Measurement precision
If complex qubit calibration models are used, then the accuracy of parameter extraction improves, but the device complexity and computational requirements increase
Solution Approach 1:
Rather than using a fully complex model for all calibration tasks, the patent applies machine learning corrections selectively to address specific deficiencies in the parametric model, such as systematic errors and higher-order effects, maintaining simplicity where it suffices while adding complexity only where needed
3Ease of manufacture
If traditional calibration methods are used, then the process is simpler to implement, but the ability to capture higher-order effects and unforeseen physics is lost
Solution Approach 1:
The patent introduces machine learning models as intermediary components that bridge the gap between simple parametric models and complex physical phenomena. These intermediaries learn to capture higher-order effects and unforeseen physics from calibration data without requiring explicit physical models for each effect
Data Source
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AI summary
A computer-implemented method for refining a qubit calibration model is described. The method comprises receiving, at a learning module, training data, wherein the training data comprises a plurality of calibration data sets, wherein each calibration data set is derived from a system comprising one or more qubits, and a plurality of parameter sets, each parameter set comprising extracted parameters obtained using a corresponding calibration data set, wherein extracting the parameters includes fitting a qubit calibration model to the corresponding calibration data set using a fitter algorithm. The method further comprises executing, at the learning module, a supervised machine learning algorithm which processes the training data to learn a perturbation to the qubit calibration model that captures one or more features in the plurality of calibration data sets that are not captured by the qubit calibration model, thereby to provide a refined qubit calibration model.