Quantum annealing is a quantum computing approach widely used for optimization and
probabilistic sampling problems. It is an alternative approach designed due to the limitations …

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

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 …

Quantum computing (QC) is anticipated to provide a speedup over classical approaches for
specific problems in optimization, simulation, and machine learning. With the advances in …

In this article, we experimentally test the performance of the recently proposed domain-wall
encoding of discrete variables Chancellor, 2019, on Ising model flux qubit quantum …

We investigate the limits of thermometry using quantum probes at thermal equilibrium within
the Bayesian approach. We consider the possibility of engineering interactions between the …

We analyse the method of encoding pairwise interactions of higher-than-binary discrete
variables (these models are sometimes referred to as discrete quadratic models) into binary …

Quantum annealing provides a way of solving optimization problems by encoding them as
Ising spin models which are implemented using physical qubits. The solution of the …

A book about turning high-degree optimization problems into quadratic optimization
problems that maintain the same global minimum (ground state). This book explores …

The probability of success of quantum annealing can be improved significantly by pausing
the annealer during its dynamics, exploiting thermal relaxation in a controlled fashion. In this …