Analog quantum kernels with operational noise outperform noiseless versions in benchmarking and non-Markovianity estimation due to increased expressivity and model complexity.
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quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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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Noise-enhanced quantum kernels on analog quantum computers
Analog quantum kernels with operational noise outperform noiseless versions in benchmarking and non-Markovianity estimation due to increased expressivity and model complexity.
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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.