Qubit Decoherence Model Determination via Data-Driven Parameter Optimization
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Solution Overview
Problem
Conventional methods for predicting the decoherence time of a qubit rely on theoretical decoherence models, leading to inaccurate predictions due to the lack of precise data-driven models.
Innovation Solution
A method is developed to determine a decoherence model of a qubit by acquiring decoherence measurement data, determining type-parameter combinations, and selecting a target decoherence model from candidate models based on this data, using techniques such as depolarization and dephasing measurements, and curve fitting to optimize model parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a decoherence model is obtained based on theoretical deducing, then the model can be constructed using physical quantities and mathematical models, but the prediction accuracy of decoherence time is insufficient
Solution Approach 1:
The patent transforms the decoherence model from a purely theoretical parameter-based approach to a data-driven approach by acquiring actual qubit measurement data and environment data, then determining model parameters through data fitting rather than theoretical deduction alone
Solution Approach 2:
The patent implements a feedback mechanism by using actual measurement data to verify and optimize the decoherence model, comparing predicted decoherence times with actual measurements, and iteratively adjusting model parameters to improve prediction accuracy
2Measurement precision
If multiple type-parameter combinations are determined, then candidate decoherence models can be selected based on measurement data, but the complexity of the determination process increases
Solution Approach 1:
The patent segments the decoherence model determination into distinct phases: acquiring measurement data, determining type-parameter combinations, selecting candidate models, and optimizing parameters. This structured segmentation makes the complex process more manageable and systematic
Solution Approach 2:
The patent introduces dynamic parameter optimization by allowing model parameters to be adjusted based on actual measurement data fitting, transforming the static theoretical model into a dynamic adaptive model that improves accuracy through data-driven parameter selection
Data Source
AI summary
The present disclosure discloses method for determining decoherence model of qubit and computer readable storage medium. The method relates to the technical field of and quantum, including: acquiring decoherence measurement data obtained by performing decoherence measurement on the qubit; determining a plurality of type-parameter combinations, the type-parameter combinations that includes combinations of model types and model parameters; determining candidate decoherence models corresponding to the plurality of type-parameter combinations based on the decoherence measurement data; and determining a target decoherence model from the candidate decoherence models corresponding to the plurality of type-parameter combinations. According to the present disclosure, the technical problem of inaccurate prediction occurs when the decoherence time of the qubit is predicted by adopting the decoherence model in the conventional technology is solved.