Quantum computers have demonstrable ability to solve problems at a scale beyond brute-
force classical simulation. Interest in quantum algorithms has developed in many areas …

In the past 20 years, impressive progress has been made both experimentally and
theoretically in superconducting quantum circuits, which provide a platform for manipulating …

Quantum annealing is a computing paradigm that has the ambitious goal of efficiently
solving large-scale combinatorial optimization problems of practical importance. However …

During the last ten years, superconducting circuits have passed from being interesting
physical devices to becoming contenders for near-future useful and scalable quantum …

Inspired by the success of Boltzmann machines based on classical Boltzmann distribution,
we propose a new machine-learning approach based on quantum Boltzmann distribution of …

Quantum annealing (QA) has been proposed as a quantum enhanced optimization heuristic
exploiting tunneling. Here, we demonstrate how finite-range tunneling can provide …

This book introduces the emerging field of quantum thermodynamics, with a focus on its
relation to quantum information and its implications for quantum computers and next …

We provide Ising formulations for many NP-complete and NP-hard problems, including all of
Karp's 21 NP-complete problems. This collects and extends mappings to the Ising model …

The study of Gaussian states has arisen to a privileged position in continuous variable
quantum information in recent years. This is due to vehemently pursued experimental …

Quantum Machine Learning bridges the gap between abstract developments in quantum
computing and the applied research on machine learning. Paring down the complexity of the …