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) …

We present a novel method to simulate the Lindblad equation, drawing on the relationship
between Lindblad dynamics, stochastic differential equations, and Hamiltonian simulations …

Abstract Quantum Boltzmann machines (QBMs) are machine-learning models for both
classical and quantum data. We give an operational definition of QBM learning in terms of …

Finding ground states of quantum many-body systems is known to be hard for both classical
and quantum computers. As a result, when Nature cools a quantum system in a low …

We upper bound and lower bound the optimal precision with which one can estimate an
unknown Hamiltonian parameter via measurements of Gibbs thermal states with a known …

Quantum computing is gaining popularity across a wide range of scientific disciplines due to
its potential to solve long-standing computational problems that are considered intractable …

Predicting observables in equilibrium states is a central yet notoriously hard question in
quantum many-body systems. In the physically relevant thermodynamic limit, certain …

A promising avenue for the preparation of Gibbs states on a quantum computer is to
simulate the physical thermalization process. The Davies generator describes the dynamics …