Fuelled by increasing computer power and algorithmic advances, machine learning
techniques have become powerful tools for finding patterns in data. Quantum systems …

Superconducting qubits are leading candidates in the race to build a quantum computer
capable of realizing computations beyond the reach of modern supercomputers. The …

Abstract Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near
future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass …

Realizing quantum speedup for practically relevant, computationally hard problems is a
central challenge in quantum information science. Using Rydberg atom arrays with up to …

Controllable, coherent many-body systems can provide insights into the fundamental
properties of quantum matter, enable the realization of new quantum phases and could …

Quantum annealing (QA) is a heuristic quantum optimization algorithm that can be used to
solve combinatorial optimization problems. In recent years, advances in quantum …

The quantum approximate optimization algorithm (QAOA) is a hybrid quantum-classical
variational algorithm designed to tackle combinatorial optimization problems. Despite its …

In the past 20 years, impressive progress has been made both experimentally and
theoretically in superconducting quantum circuits, which provide a platform for manipulating …

Faster algorithms for combinatorial optimization could prove transformative for diverse areas
such as logistics, finance and machine learning. Accordingly, the possibility of quantum …

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