Digital quantum computers provide a computational framework for solving the Schrödinger
equation for a variety of many‐particle systems. Quantum computing algorithms for the …

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 Machine Learning bridges the gap between abstract developments in quantum
computing and the applied research on machine learning. Paring down the complexity of the …

The model of adiabatic quantum computation is a relatively recent model of quantum
computation that has attracted attention in the physics and computer science communities …

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 …

The performance of the quantum approximate optimization algorithm is evaluated by using
three different measures: the probability of finding the ground state, the energy expectation …

A thorough exposition of quantum computing and the underlying concepts of quantum
physics, with explanations of the relevant mathematics and numerous examples. The …

Magnetic refrigeration around the liquid-helium temperature plays a critical role in many
technological sectors. Even if gallium gadolinium garnet (GGG) has been regarded as the …

We show that the NP-hard quadratic unconstrained binary optimization (QUBO) problem on
a graph G can be solved using an adiabatic quantum computer that implements an Ising …

Transcription factors regulate gene expression, but how these proteins recognize and
specifically bind to their DNA targets is still debated. Machine learning models are effective …