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

Over the past several decades, quantum information science has emerged to seek answers
to the question: can we gain some advantage by storing, transmitting and processing …

The physics of quantum mechanics is the inspiration for, and underlies, quantum
computation. As such, one expects physical intuition to be highly influential in the …

Adiabatic quantum computing (AQC) started as an approach to solving optimization
problems and has evolved into an important universal alternative to the standard circuit …

The model of adiabatic quantum computation is a relatively recent model of quantum
computation that has attracted attention in the physics and computer science communities …

The recent success in quantum annealing, ie, optimization of the cost or energy functions of
complex systems utilizing quantum fluctuations is reviewed here. The concept is introduced …

A quantum walk is a time-homogeneous quantum-mechanical process on a graph defined
by analogy to classical random walk. The quantum walker is a particle that moves from a …

Quantum annealing is a general strategy for solving difficult optimization problems with the
aid of quantum adiabatic evolution. Both analytical and numerical evidence suggests that …

The difficulty of simulating quantum systems, well known to quantum chemists, prompted the
idea of quantum computation. One can avoid the steep scaling associated with the exact …

Finding the ground state of a quantum many-body system is a fundamental problem in
quantum physics. In this work, we give a classical machine learning (ML) algorithm for …