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 …
The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic …
G Wendin - Reports on Progress in Physics, 2017 - iopscience.iop.org
During the last ten years, superconducting circuits have passed from being interesting physical devices to becoming contenders for near-future useful and scalable quantum …
F Neukart, G Compostella, C Seidel, D Von Dollen… - Frontiers in …, 2017 - frontiersin.org
Quantum annealing algorithms belong to the class of metaheuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing …
Hybrid quantum-classical algorithms are central to much of the current research in quantum computing, particularly when considering the noisy intermediate-scale quantum (NISQ) era …
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 …
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 …
E Farhi, AW Harrow - arXiv preprint arXiv:1602.07674, 2016 - arxiv.org
The Quantum Approximate Optimization Algorithm (QAOA) is designed to run on a gate model quantum computer and has shallow depth. It takes as input a combinatorial …