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Concentration bounds for quantum states and limitations on the QAOA from polynomial approximations

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arxiv 2209.02715 v3 pith:Z34UH7QI submitted 2022-09-06 quant-ph cs.CCmath-phmath.MP

classification quant-phcs.CCmath-phmath.MP
keywords statesqaoaquantumconcentrationdenseapproximationsboundshamiltonian
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abstract

We prove concentration bounds for the following classes of quantum states: (i) output states of shallow quantum circuits, answering an open question from [DPMRF22]; (ii) injective matrix product states; (iii) output states of dense Hamiltonian evolution, i.e. states of the form $e^{\iota H^{(p)}} \cdots e^{\iota H^{(1)}} |\psi_0\rangle$ for any $n$-qubit product state $|\psi_0\rangle$, where each $H^{(i)}$ can be any local commuting Hamiltonian satisfying a norm constraint, including dense Hamiltonians with interactions between any qubits. Our proofs use polynomial approximations to show that these states are close to local operators. This implies that the distribution of the Hamming weight of a computational basis measurement (and of other related observables) concentrates. An example of (iii) are the states produced by the quantum approximate optimisation algorithm (QAOA). Using our concentration results for these states, we show that for a random spin model, the QAOA can only succeed with negligible probability even at super-constant level $p = o(\log \log n)$, assuming a strengthened version of the so-called overlap gap property. This gives the first limitations on the QAOA on dense instances at super-constant level, improving upon the recent result [BGMZ22].

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Cited by 2 Pith papers

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

  1. Weak Poincar\'e Inequalities via Approximate Stochastic Localization: Application to Sampling the Sherrington-Kirkpatrick Model

    math.PR 2026-07 conditional novelty 7.0 of 10

    Approximate stochastic localization plus conductance transfers yield a weak Poincaré inequality for the SK model at β < 1/2, enabling efficient Glauber sampling from a warm start.

  2. Quantum-informed surrogate sampling for combinatorial optimization

    quant-ph 2026-07 conditional novelty 6.0 of 10

    QISS classically samples a pairwise model built from O(N) low-weight QAOA correlators and outperforms standard QAOA at larger depths on MaxCut and MIS benchmarks.

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