Quantum Gate Bayesian Tuning for Low-Data Calibration
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Solution Overview
Problem
Current methods for tuning quantum gates in quantum computers are inefficient, requiring substantial input data and not effectively utilizing prior information to reduce data requirements.
Innovation Solution
A Bayesian method for tuning quantum gates that involves interrogating qubits using stored control-parameter values, computing an objective function, updating control-parameter values, expanding the prior distribution to incorporate uncertainty, and re-interrogating qubits to refine the objective function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional predetermined experiment sweeps are used to tune quantum gates, then comprehensive parameter coverage is achieved, but the amount of data required increases significantly
Solution Approach 1:
The patent applies preliminary action by using a prior distribution to encode existing knowledge about quantum gate parameters before actual measurement. This prior distribution is constructed based on previous experimental results or theoretical expectations, allowing the system to start with informed guesses about parameter values. The prior distribution is then updated with new measurement data through Bayesian inference, progressively refining the parameter estimates without requiring exhaustive predetermined experiment sweeps.
Solution Approach 2:
The patent implements feedback through the Bayesian updating process where measurement results are continuously fed back to update the prior distribution. The posterior distribution from one iteration becomes the prior for the next iteration, creating a closed-loop system that progressively refines parameter estimates. This feedback mechanism allows the system to adaptively focus measurements on the most uncertain parameters, reducing the total data amount needed compared to predetermined sweeps that treat all parameters equally.
2Reliability
If exhaustive parameter sweeps are performed to ensure accurate quantum gate tuning, then tuning reliability improves, but the time required for calibration increases
Solution Approach 1:
The prior distribution serves as preliminary action by pre-encoding expected parameter ranges and uncertainties before calibration begins. This allows the system to avoid exhaustive searches and instead focus measurements on regions of parameter space that are most likely to contain the true values, based on previous knowledge. The preliminary construction of the prior distribution significantly reduces calibration time while maintaining reliability through the systematic updating process.
Solution Approach 2:
The patent applies dynamics by making the measurement strategy adaptive rather than static. The Bayesian updating process dynamically adjusts the prior distribution based on incoming measurement data, allowing the calibration process to evolve and focus on the most critical parameters. This dynamic approach replaces static predetermined sweeps with an adaptive process that maintains reliability while reducing calibration time by concentrating resources on uncertain parameters.
3Productivity
If prior information is not reused in the tuning process, then the methodology remains simple, but data requirements and computational resources increase
Solution Approach 1:
The prior distribution represents preliminary action by preparing and storing knowledge about quantum gate parameters before the tuning process begins. This prior information can come from previous experiments, theoretical models, or manufacturer specifications. By having this information prepared in advance as a probability distribution, the system can efficiently reuse it during tuning without requiring simple brute-force methods, thereby improving productivity while managing complexity through structured data representation.
Solution Approach 2:
The patent applies parameter changes by transforming prior knowledge into a probabilistic parameter distribution that can be systematically updated. The prior distribution parameters (mean, variance) are changed and refined through Bayesian updating as new data arrives. This parameter transformation approach allows the system to efficiently incorporate prior information and improve tuning efficiency while maintaining manageable complexity through the mathematical framework of probability distributions.
Data Source
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AI summary
A method for tuning a quantum gate of a quantum computer comprises interrogating one or more qubits of the quantum computer using stored control-parameter values and yielding new data. The method further comprises computing an objective function quantifying operational quality of the quantum gate at the stored control-parameter values, such computing employing the new data in addition to a prior distribution over features used to compute the objective function. Here, the prior distribution may be obtained by previous adaptive or non-adaptive interrogation of the one or more qubits, for instance. The method further comprises updating the stored control-parameter values, expanding the prior distribution to incorporate uncertainty in the objective function at the updated control-parameter values, re-interrogating the one or more qubits using the updated control-parameter values, and re-computing the objective function using the expanded prior distribution.