Computational models are an essential tool for the design, characterization, and discovery
of novel materials. Computationally hard tasks in materials science stretch the limits of …

We design a quantum algorithm for ground state preparation in the early fault tolerant
regime. As a Monte Carlo style quantum algorithm, our method features a Lindbladian …

We provide a systematic framework for constructing generic models of nonequilibrium
quantum dynamics with a target stationary (mixed) state. Our framework identifies (almost) …

The steady states of dynamical processes can exhibit stable nontrivial phases, which can
also serve as fault-tolerant classical or quantum memories. For Markovian quantum …

Preparing ground states and thermal states is essential for simulating quantum systems on
quantum computers. Despite the hope for practical quantum advantage in quantum …

This work uncovers a fundamental connection between doped stabilizer states, a concept
from quantum information theory, and the structure of eigenstates in perturbed many-body …

We show that thermal states of local Hamiltonians are separable above a constant
temperature. Specifically, for a local Hamiltonian $ H $ on a graph with degree $\mathfrak …

We propose a polynomial-time algorithm for preparing the Gibbs state of the two-
dimensional toric code Hamiltonian at any temperature, starting from any initial condition …

Gibbs sampling is a crucial computational technique used in physics, statistics, and many
other scientific fields. For classical Hamiltonians, the most commonly used Gibbs sampler is …

Sampling tasks are a natural class of problems for quantum computers due to the
probabilistic nature of the Born rule. Sampling from useful distributions on noisy quantum …