aiqbench analysis
This paper establishes a rigorous quantum fluctuation-response inequality linking observable expectation differences to relative entropy, offering tighter non-asymptotic bounds for statistical errors in hypothesis testing. The work extends these theoretical tools beyond physics into thermodynamics and provides new insights for the generalizability of machine learning algorithms within a quantum framework.
Quantum Fluctuation-Response Inequality and Its Application in Quantum Hypothesis Testing
arXiv:2203.10501v2 Announce Type: replace Abstract: We establish a general quantum fluctuation-response inequality that bounds the difference in expectation values of an observable between two quantum states via the quantum relative entropy. For observables with bounded spectra, we further strengthen this result by exploiting the sub-Gaussian property, and explicitly relate our bound to the sub-Gaussian norm of the observable. This allows us to derive a novel non-asymptotic bound on the sum of statistical errors in quantum hypothesis testing, which complements and can be more informative than existing bounds. We also demonstrate the versatility of our results through problems such as thermodynamic hypothesis testing and quantum speed limits in physics, and generalizability of algorithms in machine learning.
Abstract:We establish a general quantum fluctuation-response inequality that bounds the difference in expectation values of an observable between two quantum states via the quantum relative entropy. For observables with bounded spectra, we further strengthen this result by exploiting the sub-Gaussian property, and explicitly relate our bound to the sub-Gaussian norm of the observable. This allows us to derive a novel non-asymptotic bound on the sum of statistical errors in quantum hypothesis testing, which complements and can be more informative than existing bounds. We also demonstrate the versatility of our results through problems such as thermodynamic hypothesis testing and quantum speed limits in physics, and generalizability of algorithms in machine learning.
Quantum Physics (quant-ph); Statistics Theory (math.ST)
arXiv:2203.10501 [quant-ph]
(or arXiv:2203.10501v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2203.10501 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 205 words
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