Product states optimize quantum $p$-spin models for large $p$

TL;DR

This paper shows that for large $p$, the maximum energy of quantum $p$-spin models can be achieved by simple product states, challenging the belief that such states must be highly entangled.

Contribution

It proves the existence of near-maximal energy product states in quantum $p$-spin models for large $p$, and demonstrates classical algorithms can optimize these Hamiltonians effectively.

Findings

Maximal energy approaches $ ext{}\sqrt{2 ext{log}6}$ as $p$ increases.

Near-maximal energy states can be product states, not entangled.

Classical algorithms can optimize large $p$ Hamiltonians effectively.

Abstract

We consider the problem of estimating the maximal energy of quantum $p$-local spin glass random Hamiltonians, the quantum analogues of widely studied classical spin glass models. Denoting by $E^*(p)$ the (appropriately normalized) maximal energy in the limit of a large number of qubits $n$, we show that $E^*(p)$ approaches $\sqrt{2\log 6}$ as $p$ increases. This value is interpreted as the maximal energy of a much simpler so-called Random Energy Model, widely studied in the setting of classical spin glasses. Our most notable and (arguably) surprising result proves the existence of near-maximal energy states which are product states, and thus not entangled. Specifically, we prove that with high probability as $n\to\infty$, for any $E<E^*(p)$ there exists a product state with energy $\geq E$ at sufficiently large constant $p$. Even more surprisingly, this remains true even when restricting to tensor products of Pauli eigenstates. Our approximations go beyond what is known from monogamy-of-entanglement style arguments -- the best of which, in this normalization, achieve approximation error growing with $n$. Our results not only challenge prevailing beliefs in physics that extremely low-temperature states of random local Hamiltonians should exhibit non-negligible entanglement, but they also imply that classical algorithms can be just as effective as quantum algorithms in optimizing Hamiltonians with large locality -- though performing such optimization is still likely a hard problem. Our results are robust with respect to the choice of the randomness (disorder) and apply to the case of sparse random Hamiltonian using Lindeberg's interpolation method. The proof of the main result is obtained by estimating the expected trace of the associated partition function, and then matching its asymptotics with the extremal energy of product states using the second moment method.