Quantum annealing (QA) is a heuristic quantum optimization algorithm that can be used to
solve combinatorial optimization problems. In recent years, advances in quantum …

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 …

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 …

Quantum annealing algorithms belong to the class of metaheuristic tools, applicable for
solving binary optimization problems. Hardware implementations of quantum annealing …

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 …

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 …

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 …

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