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Obstacles to State Preparation and Variational Optimization from Symmetry Protection

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arxiv 1910.08980 v1 pith:LUBSVVDV submitted 2019-10-20 quant-ph cond-mat.str-el

Obstacles to State Preparation and Variational Optimization from Symmetry Protection

classification quant-ph cond-mat.str-el
keywords qaoasymmetryquantumstatesalgorithmhamiltonianslocallow-energy
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Local Hamiltonians with topological quantum order exhibit highly entangled ground states that cannot be prepared by shallow quantum circuits. Here, we show that this property may extend to all low-energy states in the presence of an on-site $\mathbb{Z}_2$ symmetry. This proves a version of the No Low-Energy Trivial States (NLTS) conjecture for a family of local Hamiltonians with symmetry protected topological order. A surprising consequence of this result is that the Goemans-Williamson algorithm outperforms the Quantum Approximate Optimization Algorithm (QAOA) for certain instances of MaxCut, at any constant level. We argue that the locality and symmetry of QAOA severely limits its performance. To overcome these limitations, we propose a non-local version of QAOA, and give numerical evidence that it significantly outperforms standard QAOA for frustrated Ising models on random 3-regular graphs.

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Cited by 3 Pith papers

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    A Lean 4 machine-verified proof establishes that depth-p QAOA on the ring of disagrees attains approximation ratio (2p+1)/(2p+2) exactly.

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

    math.PR 2026-07 conditional novelty 7.0

    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.

  3. Compositional Quantum Heuristics for Max-Clique Detection

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    Compositional quantum circuits with symmetry-induced invariant losses produce trainable equivariant quantum GNNs that generalize on max-clique problems and improve hybrid recursive search accuracy and scalability.