mlsanityData to 7 Oct 2026

Toward Instance-Optimal State Certification With Incoherent Measurements

arXiv 2102.13098 ↗First posted 25 Feb 2021quant-ph cs.DS cs.LGcited by 12

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Abstract (arXiv)

We revisit the basic problem of quantum state certification: given copies of unknown mixed state ρ∈mathbbCd× d and the description of a mixed state σ, decide whether σ = ρ or |σ - ρ|_mathsftr ≥ ε. When σ is maximally mixed, this is mixedness testing, and it is known that Omega(dTheta(1)/ε2) copies are necessary, where the exact exponent depends on the type of measurements the learner can make [OW15, BCL20], and in many of these settings there is a matching upper bound [OW15, BOW19, BCL20]. Can one avoid this dTheta(1) dependence for certain kinds of mixed states σ, e.g. ones which are approximately low rank? More ambitiously, does there exist a simple functional f:mathbbCd× d→ℝ≥ 0 for which one can show that Theta(f(σ)/ε2) copies are necessary and sufficient for state certification with respect to any σ? Such instance-optimal bounds are known in the context of classical distribution testing, e.g. [VV17]. Here we give the first bounds of this nature for the quantum setting, showing (up to log factors) that the copy complexity for state certification using nonadaptive incoherent measurements is essentially given by the copy complexity for mixedness testing times the fidelity between σ and the maximally mixed state. Surprisingly, our bound differs substantially from instance optimal bounds for the classical problem, demonstrating a qualitative difference between the two settings.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 4 of its 53 references that are in the map. First posted on arXiv 25 Feb 2021.

Builds on

4 of 53 references.

References · 4

  1. 2021Information-theoretic bounds on quantum advantage in machine learningcited by 95
  2. 2016Efficient quantum tomography IIcited by 54
  3. 2020Entanglement is Necessary for Optimal Quantum Property Testingcited by 22
  4. 2017Quantum state certificationcited by 13

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Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 4; 2025: 4.

12 papers.