Optimization, sampling and machine learning are topics of broad interest that have inspired
significant developments and new approaches in quantum computing. One such approach …

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

Quantum computers promise to solve certain computational problems much faster than
classical computers. However, current quantum processors are limited by their modest size …

Quantum enhanced optimization of classical cost functions is a central theme of quantum
computing due to its high potential value in science and technology. The variational …

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 variational optimization has been posed as an alternative to solve optimization
problems faster and at a larger scale than what classical methods allow. In this paper we …

Branch-and-bound is a widely used technique for solving combinatorial optimization
problems where one has access to two procedures: a branching procedure that splits a set …

We present a highly efficient quantum circuit for performing continuous time quantum walks
(CTQWs) over an exponentially large set of combinatorial objects, provided that the objects …

We apply digitized quantum annealing (QA) and quantum approximate optimization
algorithm (QAOA) to a paradigmatic task of supervised learning in artificial neural networks …

We utilize the theory of local amplitude transfer (LAT) to gain insights into quantum walks
(QWs) and quantum annealing (QA) beyond the adiabatic theorem. By representing the …