mlsanityData to 7 Oct 2026

Entanglement is Necessary for Optimal Quantum Property Testing

arXiv 2004.07869 ↗First posted 16 Apr 2020quant-ph cs.DScited by 22

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Abstract (arXiv)

There has been a surge of progress in recent years in developing algorithms for testing and learning quantum states that achieve optimal copy complexity. Unfortunately, they require the use of entangled measurements across many copies of the underlying state and thus remain outside the realm of what is currently experimentally feasible. A natural question is whether one can match the copy complexity of such algorithms using only independent---but possibly adaptively chosen---measurements on individual copies. We answer this in the negative for arguably the most basic quantum testing problem: deciding whether a given d-dimensional quantum state is equal to or ε-far in trace distance from the maximally mixed state. While it is known how to achieve optimal O(d/ε2) copy complexity using entangled measurements, we show that with independent measurements, Omega(d4/3/ε2) is necessary, even if the measurements are chosen adaptively. This resolves a question of Wright. To obtain this lower bound, we develop several new techniques, including a chain-rule style proof of Paninski's lower bound for classical uniformity testing, which may be of independent interest.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Apr 2020.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 4; 2024: 4; 2025: 5.

22 papers.

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  1. 2021Quantum advantage in learning from experimentscited by 186
  2. 2021Information-theoretic bounds on quantum advantage in machine learningcited by 95
  3. 2021Exponential separations between learning with and without quantum memorycited by 50
  4. 2021A Hierarchy for Replica Quantum Advantagecited by 16
  5. 2022The Complexity of NISQcited by 13
  6. 2021Toward Instance-Optimal State Certification With Incoherent Measurementscited by 12
  7. 2022When Does Adaptivity Help for Quantum State Learning?cited by 12
  8. 2022Tight Bounds for Quantum State Certification with Incoherent Measurementscited by 11
  9. 2023Tight bounds on Pauli channel learning without entanglementcited by 7
  10. 2022Lower Bounds for Learning Quantum States with Single-Copy Measurementscited by 6
  11. 2023On the Role of Entanglement and Statistics in Learningcited by 5
  12. 2023Transition Role of Entangled Data in Quantum Machine Learningcited by 5
10 more
  1. 2024On the sample complexity of purity and inner product estimationcited by 4
  2. 2023Spacetime-Efficient Low-Depth Quantum State Preparation with Applicationscited by 2
  3. 2024An optimal tradeoff between entanglement and copy complexity for state tomographycited by 2
  4. 2025Instance-Optimal Quantum State Certification with Entangled Measurementscited by 1
  5. 2024New Bounds on Quantum Sample Complexity of Measurement Classescited by 0
  6. 2024Adaptivity can help exponentially for shadow tomographycited by 0
  7. 2025Purest Quantum State Identificationcited by 0
  8. 2025Sample-optimal learning of quantum states using gentle measurementscited by 0
  9. 2025Efficient learning of bosonic Gaussian unitariescited by 0
  10. 2025Non-iid hypothesis testing: from classical to quantumcited by 0