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A Hierarchy for Replica Quantum Advantage

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arxiv 2111.05874 v2 pith:TUL2HVQT submitted 2021-11-10 quant-ph cs.CCcs.ITcs.LGmath.IT

classification quant-phcs.CCcs.ITcs.LGmath.IT
keywords replicasentangledhierarchylearnmakemeasurementmeasurementsproperty
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

We prove that given the ability to make entangled measurements on at most $k$ replicas of an $n$-qubit state $\rho$ simultaneously, there is a property of $\rho$ which requires at least order $2^n$ measurements to learn. However, the same property only requires one measurement to learn if we can make an entangled measurement over a number of replicas polynomial in $k, n$. Because the above holds for each positive integer $k$, we obtain a hierarchy of tasks necessitating progressively more replicas to be performed efficiently. We introduce a powerful proof technique to establish our results, and also use this to provide new bounds for testing the mixedness of a quantum state.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Instance-Optimal Quantum State Certification with Entangled Measurements

    quant-ph 2025-07 accept novelty 9.0 of 10

    Quantum state certification with entangled measurements has copy complexity Θ~(∥σ*∥_{1/2}/ε²), where σ* is the hypothesis state with a small tail of eigenvalues removed.

  2. High-rate qLDPC processors

    quant-ph 2026-07 conditional novelty 8.0 of 10

    Non-abelian "mitten" qLDPC codes achieve 20% encoding rate with distances 10-24 on 150-975 qubits, and simulations indicate fault-tolerant processors sustaining ~10^10 logical operations at 0.1% physical error rate.

  3. Instance-optimal high-precision shadow tomography with few-copy measurements: A metrological approach

    quant-ph 2026-02 conditional novelty 8.0 of 10

    High-precision shadow tomography of unknown quantum states has sample complexity Θ~(Γ_p/ε²), with Γ_p characterized by the inverse Fisher information matrix of the optimal single-copy measurement.

  4. Measuring Less to Learn More: Quadratic Speedup in learning Nonlinear Properties of Quantum Density Matrices

    quant-ph 2025-09 conditional novelty 7.0 of 10

    A quantum algorithm estimates Tr(ρ^k O) with O(√k) queries to a purification-preparing unitary, quadratically faster than sample-based methods, with a claimed matching lower bound.

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