Quantum Processor Calibration Using Uncertainty-Guided Measurements
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Solution Overview
Problem
Calibrating quantum processors is a time-consuming and costly process due to the need for extensive data collection and characterization of individual programmable elements and their interactions, especially as the number of devices increases.
Innovation Solution
A hybrid computing system employs a digital processor to optimize the measurement schedule for calibrating quantum devices by selecting a subset of measurements based on predicted uncertainty reduction, using machine learning to iteratively refine model parameters until a termination criterion is met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive data collection and characterization of individual programmable elements and their interactions is performed to calibrate quantum processors, then calibration accuracy is improved, but calibration time and cost increase significantly
Solution Approach 1:
The calibration process is segmented into two distinct phases: a comprehensive initial calibration that characterizes all programmable elements and their interactions to build a detailed model, followed by a streamlined iterative calibration that uses machine learning to select only the most informative measurements. This segmentation allows the system to achieve high calibration accuracy while significantly reducing the time required for repeated calibrations.
Solution Approach 2:
The system performs preliminary comprehensive characterization of all quantum processor elements before actual calibration operations. This preliminary action builds a detailed model of the system that includes all programmable elements and their interactions, which then serves as the foundation for efficient iterative calibration using machine learning-based measurement selection.
2Measurement precision
If comprehensive characterization of all programmable elements and their interactions is performed, then model accuracy is improved, but the number of measurements required increases
Solution Approach 1:
The system implements feedback through machine learning algorithms that continuously evaluate the calibration model's uncertainty and select the next most informative measurements. This feedback loop allows the system to adaptively focus measurements on areas where the model is least certain, achieving high model accuracy while minimizing the total number of measurements required.
Solution Approach 2:
The system changes the parameter of measurement selection from exhaustive comprehensive measurement to intelligent selective measurement based on model uncertainty. By using machine learning to dynamically determine which measurements provide the most value, the system achieves high model accuracy with significantly improved measurement efficiency.
3Productivity
If machine learning is used to optimize measurement selection, then measurement efficiency is improved, but system complexity increases
Solution Approach 1:
The system introduces a machine learning-based measurement selection module as an intermediary between the quantum processor and the calibration process. This intermediary analyzes the current calibration model and selects the most informative measurements, improving measurement efficiency while keeping the quantum processor itself unchanged and the added complexity isolated to a software layer.
Data Source
AI summary
Methods and systems for calibrating quantum processors are discussed. A model of a portion of the processor to be calibrated has one or more determinable parameters and an uncertainty for the determinable parameter(s). A measurement procedure is iteratively performed by selecting a subset of possible measurements and generating predicted measurement outcomes and predicted uncertainties for the determinable parameter for each measurement in the subset of possible measurements. Based on the predicted reduction in uncertainty for the determinable parameter, one or more measurements is selected. Instructions are transmitted to the quantum processor to perform the selected measurements, and the results are returned to update the model of the portion of the processor to be calibrated. Once a termination criteria is met, a calibrated value is generated for the determinable parameter. Compensating signals can be applied to devices of the quantum processor to calibrate the devices.