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This paper establishes that nonadaptive single-copy protocols achieve the same asymptotic sample complexity as adaptive strategies for purity and product testing in quantum mechanics. By proving that the positive-partial-transpose (PPT) relaxation yields no loss in performance for these fundamental tasks, the work simplifies the theoretical characterization of measurement bounds using basic symmetric subspace identities. These findings provide a rigorous foundation for optimizing experimental designs in quantum state tomography without the complexity of adaptive feedback loops.
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Tight Bounds for Purity and Product Testing from Partial Transposition

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arXiv:2608.27217v1 Announce Type: new Abstract: Coherent measurements across multiple copies of an unknown quantum state can substantially reduce the number of samples required to learn its properties, but remain experimentally challenging. Current experiments typically prepare and measure one copy at a time, potentially adapting later measurements to earlier outcomes. A central challenge is adaptivity, which makes the space of possible measurement strategies difficult to characterize. The positive-partial-transpose (PPT) relaxation bypasses this complexity by considering a larger, mathematically tractable class of measurements, at the risk of weakening the resulting bounds. Here we show, surprisingly, that the relaxation loses nothing at the level of asymptotic sample complexity for two fundamental tasks: purity testing and product testing. In both cases, lower bounds against the full class of PPT measurements are matched by nonadaptive single-copy protocols. Moreover, our proof requires only basic symmetric subspace identities, providing a simple route to sharp lower bounds for adaptive single-copy measurements.

Abstract:Coherent measurements across multiple copies of an unknown quantum state can substantially reduce the number of samples required to learn its properties, but remain experimentally challenging. Current experiments typically prepare and measure one copy at a time, potentially adapting later measurements to earlier outcomes. A central challenge is adaptivity, which makes the space of possible measurement strategies difficult to characterize. The positive-partial-transpose (PPT) relaxation bypasses this complexity by considering a larger, mathematically tractable class of measurements, at the risk of weakening the resulting bounds. Here we show, surprisingly, that the relaxation loses nothing at the level of asymptotic sample complexity for two fundamental tasks: purity testing and product testing. In both cases, lower bounds against the full class of PPT measurements are matched by nonadaptive single-copy protocols. Moreover, our proof requires only basic symmetric subspace identities, providing a simple route to sharp lower bounds for adaptive single-copy measurements.

arXiv:2608.27217 [quant-ph]

(or arXiv:2608.27217v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2608.27217 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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