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This paper presents rigorous mathematical results on the asymptotic limits of state localization in quantum sequences using collective measurements and square-root measurements. By deriving exact integral formulas for optimal success probabilities, it advances the theoretical understanding of detecting transient changes within stationary quantum output streams. The work relies heavily on advanced functional analysis tools like Toeplitz symbols and Følner reductions to solve fundamental problems in quantum hypothesis testing.
Quantum Change Intervals: Exact Asymptotic Localization with Collective Measurements
arXiv:2608.24543v1 Announce Type: new Abstract: We study exact-label minimum-error localization of a transient, calibrated pure-state change occupying one nonempty contiguous interval in an otherwise stationary sequence of independent outputs, allowing arbitrary collective measurements. Let $c=|\langle 0|\psi\rangle|$ be fixed as the sequence length grows. For each fixed known interval length $i$, as the number $N$ of admissible translations tends to infinity, the corresponding Toeplitz symbol yields an exact square-root integral for the asymptotic optimal success probability, and the square-root measurement (SRM) attains the same limit. If the known length $i_n$ and $N_n=n-i_n+1$ both diverge, with no restriction on their ratio, the optimal and SRM success probabilities converge to the one-dimensional Toeplitz functional at the effective compound overlap $c^2$, namely $p_1(c^2)$. Under a uniform prior over all nonempty intervals, the unknown-length physical Gram kernel is not globally two-dimensional Toeplitz because of gap-dependent corrections. A triangular F{\o}lner reduction and an exceptional-sector Gram transfer theorem extend the comparison-kernel limits to the optimal and SRM success probabilities of the full physical ensemble, yielding $p_1(c)^2$. After adding a no-change hypothesis with fixed prior $\pi_0$, while retaining the uniform conditional distribution over anomalous intervals, the optimal joint Bayes limit is $\pi_0+(1-\pi_0)L$, where $L$ is the corresponding conditional localization limit; the weighted SRM for the augmented ensemble is not analyzed. Finite-size semidefinite programs and full dense physical-Gram SRM computations illustrate the asymptotic results.
Abstract:We study exact-label minimum-error localization of a transient, calibrated pure-state change occupying one nonempty contiguous interval in an otherwise stationary sequence of independent outputs, allowing arbitrary collective measurements. Let $c=|\langle 0|\psi\rangle|$ be fixed as the sequence length grows. For each fixed known interval length $i$, as the number $N$ of admissible translations tends to infinity, the corresponding Toeplitz symbol yields an exact square-root integral for the asymptotic optimal success probability, and the square-root measurement (SRM) attains the same limit. If the known length $i_n$ and $N_n=n-i_n+1$ both diverge, with no restriction on their ratio, the optimal and SRM success probabilities converge to the one-dimensional Toeplitz functional at the effective compound overlap $c^2$, namely $p_1(c^2)$. Under a uniform prior over all nonempty intervals, the unknown-length physical Gram kernel is not globally two-dimensional Toeplitz because of gap-dependent corrections. A triangular Følner reduction and an exceptional-sector Gram transfer theorem extend the comparison-kernel limits to the optimal and SRM success probabilities of the full physical ensemble, yielding $p_1(c)^2$. After adding a no-change hypothesis with fixed prior $\pi_0$, while retaining the uniform conditional distribution over anomalous intervals, the optimal joint Bayes limit is $\pi_0+(1-\pi_0)L$, where $L$ is the corresponding conditional localization limit; the weighted SRM for the augmented ensemble is not analyzed. Finite-size semidefinite programs and full dense physical-Gram SRM computations illustrate the asymptotic results.
arXiv:2608.24543 [quant-ph]
(or arXiv:2608.24543v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2608.24543 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 311 words
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