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

Quantum mechanics, the subfield of physics that describes the behavior of very small
(quantum) particles, provides the basis for a new paradigm of computing. First proposed in …

Variational quantum machine learning is an extensively studied application of near-term
quantum computers. The success of variational quantum learning models crucially depends …

Using quantum devices supported by classical computational resources is a promising
approach to quantum-enabled computation. One powerful example of such a hybrid …

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 …

Recent experiments with increasingly larger numbers of qubits have sparked renewed
interest in adiabatic quantum computation, and in particular quantum annealing. A central …

We demonstrate that the performance of a quantum annealer on hard random Ising
optimization problems can be substantially improved using quantum annealing correction …

Quantum annealing provides a promising route for the development of quantum optimization
devices, but the usefulness of such devices will be limited in part by the range of …

Physical implementations of quantum annealing unavoidably operate at finite temperatures.
We point to a fundamental limitation of fixed finite temperature quantum annealers that …

Fermion-to-qubit mappings that preserve geometric locality are especially useful for
simulating lattice fermion models (eg, the Hubbard model) on a quantum computer. They …