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 …

Adiabatic quantum computing (AQC) started as an approach to solving optimization
problems and has evolved into an important universal alternative to the standard circuit …

Quantum annealing is a computing paradigm that has the ambitious goal of efficiently
solving large-scale combinatorial optimization problems of practical importance. However …

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 …

We present a direct comparison between QAOA (Quantum Alternating Operator Ansatz), and
QA (Quantum Annealing) on 127 qubit problem instances. QAOA with p= 1, 2 rounds is …

Mainstream machine-learning techniques such as deep learning and probabilistic
programming rely heavily on sampling from generally intractable probability distributions …

This article proposes the basic way of behaving of the quantum Sherrington-Kirkpatrick (SK)
model at nothing and limited temperatures is examined. Through the investigation of the …

Combinatorial optimization problems are crucial for widespread applications but remain
difficult to solve on a large scale with conventional hardware. Novel optical platforms, known …

Quantum annealers are designed to utilize quantum tunneling to find good solutions to hard
optimization problems. When constructing a family of synthetic inputs to test the potential of a …

We investigate alternative annealing schedules on the current generation of quantum-
annealing hardware (the D-Wave 2000Q), which includes the use of forward and reverse …