Quantum kernel ridge regression shows double descent in test risk, with the interpolation peak suppressible by regularization, via random matrix theory asymptotics in the high-dimensional limit.
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quant-ph 2years
2026 2verdicts
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Binary decision trees enable cost-effective multinomial classifiers from quantum binary models, matching other methods' accuracy with at most logarithmic overhead in the number of classes.
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Double Descent in Quantum Kernel Ridge Regression
Quantum kernel ridge regression shows double descent in test risk, with the interpolation peak suppressible by regularization, via random matrix theory asymptotics in the high-dimensional limit.
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Divide et impera: hybrid multinomial classifiers from quantum binary models
Binary decision trees enable cost-effective multinomial classifiers from quantum binary models, matching other methods' accuracy with at most logarithmic overhead in the number of classes.