Ising machines are hardware solvers that aim to find the absolute or approximate ground
states of the Ising model. The Ising model is of fundamental computational interest because …

During the last ten years, superconducting circuits have passed from being interesting
physical devices to becoming contenders for near-future useful and scalable quantum …

Quantum annealing algorithms belong to the class of metaheuristic tools, applicable for
solving binary optimization problems. Hardware implementations of quantum annealing …

Quantum computers are designed to outperform standard computers by running quantum
algorithms. Areas in which quantum algorithms can be applied include cryptography, search …

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

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

Combinatorial optimization is anticipated to be one of the primary use cases for quantum
computation in the coming years. The Quantum Approximate Optimization Algorithm and …

We investigate a hybrid quantum-classical solution method to the mean-variance portfolio
optimization problems. Starting from real financial data statistics and following the principles …

Despite the development of increasingly capable quantum computers, an experimental
demonstration of a provable algorithmic quantum speedup employing today's non-fault …

The observation of an unequivocal quantum speedup remains an elusive objective for
quantum computing. A more modest goal is to demonstrate a scaling advantage over a class …