With quantum computing technologies nearing the era of commercialization and quantum
supremacy, machine learning (ML) appears as one of the promising'killer'applications …

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

There are well-developed theoretical tools to analyze how quantum dynamics can solve
computational problems by varying Hamiltonian parameters slowly, near the adiabatic limit …

An increase in the efficiency of sampling from Boltzmann distributions would have a
significant impact on deep learning and other machine-learning applications. Recently …

Mainstream machine-learning techniques such as deep learning and probabilistic
programming rely heavily on sampling from generally intractable probability distributions …

Quantum annealing is a new-generation tool of information technology, which helps in
solving combinatorial optimization problems with high precision, based on the concepts of …

In this paper I propose a new method of encoding discrete variables into Ising model qubits
for quantum optimisation. The new method is based on the physics of domain walls in one …

Abstract Machine learning has been presented as one of the key applications for near-term
quantum technologies, given its high commercial value and wide range of applicability. In …

Artificial neural networks are at the heart of modern deep learning algorithms. We describe
how to embed and train a general neural network in a quantum annealer without introducing …

I describe how real quantum annealers may be used to perform local (in state space)
searches around specified states, rather than the global searches traditionally implemented …