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

Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …

Quantum information technologies, on the one hand, and intelligent learning systems, on the
other, are both emergent technologies that are likely to have a transformative impact on our …

With the advent of real-world quantum computing, the idea that parametrized quantum
computations can be used as hypothesis families in a quantum-classical machine learning …

Quantum reinforcement learning is an emerging field at the intersection of quantum
computing and machine learning. While we intend to provide a broad overview of the …

Recent advance on quantum devices realizes an artificial quantum spin system known as
the D-Wave 2000Q, which implements the Ising model with tunable transverse field. In this …

Quantum algorithms have been successfully applied to provide computational speed ups to
various machine-learning tasks and methods. A notable exception to this has been deep …

Abstract Deep Reinforcement Learning (RL) has considerably advanced over the past
decade. At the same time, state-of-the-art RL algorithms require a large computational …

Free energy-based reinforcement learning (FERL) with clamped quantum Boltzmann
machines (QBM) was shown to significantly improve the learning efficiency compared to …

Quantum annealing is a generic solver for optimization problems that uses fictitious quantum
fluctuation. The most groundbreaking progress in the research field of quantum annealing is …