Efficient Local Learning of Quantum Gibbs States 🔍

Anthony Chen (UC Berkeley and MIT)
https://simons.berkeley.edu/talks/anthony-chen-uc-berkeley-mit-2025-05-27
Quantum Algorithms, Complexity, and Fault Tolerance Reunion

Learning the Hamiltonian underlying a quantum many-body system in thermal equilibrium is a fundamental task in quantum learning theory and experimental sciences. To learn the Gibbs state of local Hamiltonians at any inverse temperature $\beta$, the state-of-the-art provable algorithms fall short of the optimal sample and computational complexity, in sharp contrast with the locality and simplicity in the classical cases. In this work, we present a learning algorithm that learns each local term of a $n$-qubit $D$-dimensional Hamiltonian to an additive error $\epsilon$ with sample complexity ~ $e^{poly(\beta)} / \beta^2\epsilon^2 \log(n)$. The protocol uses parallelizable local quantum measurements that act within bounded regions of the lattice and near-linear-time classical post-processing. Thus, our complexity is near optimal with respect to $n,\epsilon$ and is polynomially tight with respect to $\beta$. We also give a learning algorithm for Hamiltonians with bounded interaction degree with sample and time complexities of similar scaling on $n$ but worse on $\beta, \epsilon$. At the heart of our algorithm is the interplay between locality, the Kubo-Martin-Schwinger condition, and the operator Fourier transform at arbitrary temperatures. Based on joint work with Anurag Anshu and Quynh T. Nguyen, [2504.02706].