In this review, after providing the basic physical concept behind quantum annealing (or
adiabatic quantum computation), we present an overview of some recent theoretical as well …

Optimization, sampling and machine learning are topics of broad interest that have inspired
significant developments and new approaches in quantum computing. One such approach …

Experiments on disordered alloys,–suggest that spin glasses can be brought into low-
energy states faster by annealing quantum fluctuations than by conventional thermal …

Quantum simulation has emerged as a valuable arena for demonstrating and understanding
the capabilities of near-term quantum computers,–. Quantum annealing, has been …

Shortcuts to adiabaticity provide fast protocols for quantum state preparation in which the
use of auxiliary counterdiabatic controls circumvents the requirement of slow driving in …

The quantum Kibble-Zurek mechanism (QKZM) predicts universal dynamical behavior near
the quantum phase transitions (QPTs). It is now well understood for the one-dimensional …

The number of topological defects created in a system driven through a quantum phase
transition exhibits a power-law scaling with the driving time. This universal scaling law is the …

The many-body physics at quantum phase transitions shows a subtle interplay between
quantum and thermal fluctuations, emerging in the low-temperature limit. In this review, we …

We explore the potential of D-Wave's quantum annealers for computing some of the basic
components required for quantum simulations of standard model physics. By implementing …

Quantum annealing is a heuristic optimization algorithm that exploits quantum evolution to
approximately find lowest energy states. Quantum annealers have scaled up in recent years …