Variational Quantum Circuit Training With Entropy-Regularized Loss
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Solution Overview
Problem
Existing hybrid quantum-classical machine learning models suffer from low prediction accuracy, failure to generalize training data, and long training times due to issues like the barren plateau problem and overfitting, particularly in noisy intermediate-scale quantum devices.
Innovation Solution
A method and system for training variational quantum circuits that incorporate an entropy-based loss term to penalize excess information, combining quality-based and entropy-based loss terms to promote convergence towards minimal sufficient statistics, using hybrid quantum-classical systems with classical processing resources to update variational parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If variational quantum circuits are trained using conventional methods, then the model can be trained, but the prediction accuracy is low and generalization fails
Solution Approach 1:
The patent modifies the loss function by adding an entropy-based regularization term that penalizes excessive confidence in predictions. This parameter change in the optimization objective prevents overfitting and improves generalization while maintaining prediction accuracy on training data.
Solution Approach 2:
The entropy measure acts as an intermediary mechanism that mediates between the quality-based loss term and the variational parameters. It provides a regularization effect that balances fitting training data well while maintaining generalization ability to unseen data.
2Productivity
If variational quantum circuits are trained with conventional loss functions, then training can proceed, but training time is long
Solution Approach 1:
The patent implements a feedback mechanism where the entropy measure of candidate outputs is continuously monitored and fed back into the loss function. This guides the optimization process more efficiently by providing additional information about the distribution of predictions, accelerating convergence.
Solution Approach 2:
The loss function is made dynamic by incorporating the entropy measure that adapts during training. The entropy-based term dynamically adjusts the optimization landscape, helping the variational parameters converge faster to optimal values.
3Measurement precision
If the variational quantum circuit captures all information from input data, then prediction accuracy improves, but overfitting occurs and generalization fails
Solution Approach 1:
The patent converts the harmful effect of excessive information capture (overfitting) into a benefit by using the entropy measure as a regularization signal. The entropy-based loss term transforms the problem of capturing too much information into a controlled optimization objective that prevents overfitting while maintaining necessary information for accurate predictions.
Solution Approach 2:
By adding the entropy-based regularization term to the loss function, the patent changes the optimization parameters to balance information capture and generalization. This parameter modification ensures the model learns meaningful patterns without memorizing training data specifics.
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AI summary
A computer-implemented method for training a machine learning model for approximating a labeling function comprising a variational quantum circuit, said method comprising establishing the variational quantum circuit, the variational quantum circuit comprising a plurality of variational quantum gates, and a plurality of encoding gates; executing the variational quantum circuit for a plurality of sample inputs; determining a candidate output for each sample input; determining a quality measure of the candidate outputs for the plurality of sample inputs in view of the labeling function to be approximated by the machine learning model; determining an entropy measure of the candidate outputs determined for each of the sample inputs; and updating the variational parameters based on an update function, the update function including a quality-based loss term based on the quality measure and an entropy-based loss term penalizing excess information on the input data encoded in the candidate output through the entropy measure.