Abstract The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising
variational quantum algorithm that aims to solve combinatorial optimization problems that …

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 …

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

Airlines today are faced with a number of large-scale scheduling problems. One such
problem is the tail-assignment problem, which is the task of assigning individual aircraft to a …

Present-day, noisy, small or intermediate-scale quantum processors—although far from fault
tolerant—support the execution of heuristic quantum algorithms, which might enable a …

Quantum annealing aims at solving optimization problems efficiently by preparing the
ground state of an Ising spin-Hamiltonian quantum mechanically. A prerequisite of building …

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 …

The minimum vertex cover problem belongs to a NP-complete problem, which is difficult to
obtain the near-optimal solution in the polynomial time range using classical algorithms. In …

Power system fault diagnosis is crucial for identifying the location and causes of faults and
providing decision-making support for power dispatchers. However, most classical methods …

We present a new quantum heuristic algorithm aimed at finding satisfying assignments for
hard K-SAT instances using a continuous time quantum walk that explicitly exploits the …