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

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 …

In this study, we developed a new method of topology optimization for truss structures by
quantum annealing. To perform quantum annealing analysis with real variables …

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 …

Quantum computers have provided a promising tool for tackling NP hard problems.
However, most of the existing work on quantum annealers assumes exclusive access to all …

The variational quantum eigensolver (VQE) is a hybrid quantum classical algorithm
designed for current and near-term quantum devices. Despite its initial success, there is a …

We develop a framework for analyzing layered quantum algorithms such as quantum
alternating operator ansätze (QAOA). In the context of combinatorial optimization, our …

By controlling quantum fluctuations via the Falk–Bruch inequality we give the first rigorous
argument for the existence of a spin-glass phase in the quantum Sherrington–Kirkpatrick …

Analog quantum algorithms are formulated in terms of Hamiltonians rather than unitary
gates and include quantum adiabatic computing, quantum annealing, and the quantum …

By exploiting the link between timeindependent Hamiltonians and thermalisation, heuristic
predictions on the performance of continuous-time quantum walks for MAX-CUT are made …