Recent advances in quantum computers are demonstrating the ability to solve problems at a
scale beyond brute force classical simulation. As such, a widespread interest in quantum …

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

Many Artificial Intelligence (AI) algorithms are inspired by physics and employ stochastic
fluctuations. We connect these physics-inspired AI algorithms by unifying them under a …

We propose a quantum annealing protocol that effectively probes the dynamics of a single
qubit on D-Wave's quantum annealing hardware. This protocol uses D-Wave'sh-gain …

Adiabatic quantum computing (AQC) is a promising approach for discrete and often NP-hard
optimization problems. Current AQCs allow to implement problems of research interest …

Quantum annealing is an efficient technology to determine ground state configurations of
discrete binary optimization problems, described through Ising Hamiltonians. Here we show …

Quantum annealing has shown promise for finding solutions to difficult optimization
problems, including protein folding. Recently, we used the D-Wave Advantage quantum …

Despite the attempts to apply a quantum annealer to Boltzmann sampling, it is still
impossible to perform accurate sampling at arbitrary temperatures. Conventional distribution …

We are interested in benchmarking both quantum annealing and classical algorithms for
minimizing quadratic unconstrained binary optimization (QUBO) problems. Such problems …

Graphical models are useful tools for describing structured high-dimensional probability
distributions. The development of efficient algorithms for generating samples thereof …