Tight Bounds for Quantum State Certification with Incoherent Measurements
Sitan Chen, Jerry Li, Brice Huang, Allen Liu
摘要
We consider the problem of quantum state certification, where we are given the description of a mixed state copies of a mixed state , and , and we are asked to determine whether or whether . When is the maximally mixed state , this is known as mixedness testing. We focus on algorithms which use incoherent measurements, i.e. which only measure one copy of at a time. Unlike those that use entangled, multi-copy measurements, these can be implemented without persistent quantum memory and thus represent a large class of protocols that can be run on current or near-term devices. For mixedness testing, there is a folklore algorithm which uses incoherent measurements and only needs copies. The algorithm is non-adaptive, that is, its measurements are fixed ahead of time, and is known to be optimal for non-adaptive algorithms. However, when the algorithm can make arbitrary incoherent measurements, the best known lower bound is only [5], and it has been an outstanding open problem to close this polynomial gap. In this work: •We settle the copy complexity of mixedness testing with incoherent measurements and show that copies are necessary. This fully resolves open questions of [15] and [5].•We show that the instance-optimal bounds for state certification to general first derived in [7] for non-adaptive measurements also hold for arbitrary incoherent measurements. Qualitatively, our results say that adaptivity does not help at all for these problems. Our results are based on new techniques that allow us to reduce the problem to understanding the concentration of certain matrix martingales, which we believe may be of independent interest.
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引用它的顶会 Paper10
- Instance-Optimal Quantum State Certification with Entangled MeasurementsRyan O'Donnell, Chirag WadhwaSTOC 2026 · 被引用 14 次
- Optimal Tradeoffs for Estimating Pauli ObservablesSitan Chen, Weiyuan Gong, Qi YeFOCS 2024 · 被引用 13 次
- When Does Adaptivity Help for Quantum State Learning?Sitan Chen, Brice Huang, Jerry Li, Allen Liu 等FOCS 2023 · 被引用 12 次
- Certifying Almost All Quantum States with Few Single-Qubit MeasurementsHsin-Yuan Huang, John Preskill, Mehdi SoleimanifarFOCS 2024 · 被引用 10 次
- An Optimal Tradeoff between Entanglement and Copy Complexity for State TomographySitan Chen, Jerry Li, Allen LiuSTOC 2024 · 被引用 9 次
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- Exponential Separations Between Learning With and Without Quantum MemorySitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry LiFOCS 2021 · 被引用 79 次
- Entanglement is Necessary for Optimal Quantum Property TestingSébastien Bubeck, Sitan Chen, Jerry LiFOCS 2020 · 被引用 33 次
- Distributed Quantum inner product estimationAnurag Anshu, Zeph Landau, Yunchao LiuSTOC 2022 · 被引用 27 次
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