Meta-learning with 24 classical complexity metrics predicts the optimal quantum encoding circuit among 9 candidates with up to 85.7% top-3 accuracy.
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Analog quantum kernels with operational noise outperform noiseless versions in benchmarking and non-Markovianity estimation due to increased expressivity and model complexity.
An analogy is drawn equating hidden units in Boltzmann machines with discrete Feynman paths, yielding quantum-circuit representations and links to inverse scattering for interpretability.
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Towards Automated Selection of Quantum Encoding Circuits via Meta-Learning
Meta-learning with 24 classical complexity metrics predicts the optimal quantum encoding circuit among 9 candidates with up to 85.7% top-3 accuracy.
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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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Analogy between Boltzmann machines and Feynman path integrals
An analogy is drawn equating hidden units in Boltzmann machines with discrete Feynman paths, yielding quantum-circuit representations and links to inverse scattering for interpretability.