A global technology arms race is underway to build evermore powerful and precise quantum
computers. Quantum computers have the potential to tackle certain quantitative problems …

Quantum computers promise to solve certain computational problems much faster than
classical computers. However, current quantum processors are limited by their modest size …

Efficient sampling from a classical Gibbs distribution is an important computational problem
with applications ranging from statistical physics over Monte Carlo and optimization …

Many simulation tasks require that one first prepare a system's Gibbs state. We present a
family of quantum circuits for variational preparation of thermal Gibbs states on a quantum …

The preparation of Gibbs thermal states is an important task in quantum computation with
applications in quantum simulation, quantum optimization, and quantum machine learning …

Sampling from distributions is an important capability for a range of tasks in science and
engineering, including in machine learning applications. Quantum computers hold promise …

Although the Hamiltonian formalism is, so far, favored for quantum computation of lattice
gauge theory, the path integral formalism would never be useless. The advantages of the …

We introduce an optimal strategy to sample quantum outcomes of local measurement strings
for isometric tensor network states. Our method generates samples based on an exact …

In 1948, Fritz John proposed a theorem stating that every convex body has a unique
maximal volume inscribed ellipsoid, known as the John ellipsoid. The John ellipsoid has …

Quantum annealing is a contender to solve combinatorial optimization problems based on
quantum dynamics. While significant efforts have been undertaken to investigate the quality …