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Circuit complexity lower bounds for quantum spin glasses

Quantum Physics 2026-07-15 v1 Disordered Systems and Neural Networks Computational Complexity Probability

Abstract

A central question in quantum information theory is the circuit complexity of states arising from standard many-body models. We study this question for quantum pp-spin glasses, random Hamiltonians whose interactions act on pp-tuples of qubits through Pauli strings. Anschuetz, Gamarnik, and Kiani (arXiv:2404.07231) showed that the optimum energy is separated from the best energy achievable by product states. This leaves open whether shallow circuits can close the gap, since even depth-one circuits can generate entanglement. We show that the entanglement needed to close the product-state gap cannot be generated at shallow depth. When the average interaction degree grows with nn, we prove that, for all sufficiently large fixed pp, any circuit preparing an nn-qubit state whose normalized energy is within a fixed positive constant of the optimum must have depth Ωp(logn)\Omega_p(\log n). In the bounded-average-degree regime, we prove a fixed-depth obstruction: for every fixed DD, a sufficiently large degree prefactor rules out depth-DD preparation of near-ground states. Both results hold uniformly over circuits with an arbitrary number of ancilla qubits. Our results give an obstruction in the spirit of the No Low-Energy Trivial States problem of Freedman and Hastings (arXiv:1301.1363), but for random quantum spin glasses rather than code-based Hamiltonians such as those of Anshu, Breuckmann, and Nirkhe (arXiv:2206.13228), whose ground states admit polynomial-size preparation circuits. This setting opens a probabilistic route to NLTS-like questions: we recast state-preparation lower bounds for random quantum Hamiltonians as uniform control of Gaussian processes indexed by shallow circuits.

Cite

@article{arxiv.2607.14384,
  title  = {Circuit complexity lower bounds for quantum spin glasses},
  author = {Omar Al-Ghattas and David Gamarnik},
  journal= {arXiv preprint arXiv:2607.14384},
  year   = {2026}
}

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58 pages