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

Quantum annealing is a quantum computing approach widely used for optimization and
probabilistic sampling problems. It is an alternative approach designed due to the limitations …

Hybrid quantum-classical algorithms are central to much of the current research in quantum
computing, particularly when considering the noisy intermediate-scale quantum (NISQ) era …

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 …

Kernel-based support vector machines (SVMs) are supervised machine learning algorithms
for classification and regression problems. We introduce a method to train SVMs on a D …

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 …

Quantum annealing has the potential to provide a speedup over classical algorithms in
solving optimization problems. Just as for any other quantum device, suppressing …

Quantum annealing has been actively researched since D-Wave Systems produced the first
commercial machine in 2011. Controlling a large fleet of automated guided vehicles is one …

Finding the exact counterdiabatic potential is, in principle, particularly demanding. Following
recent progress about variational strategies to approximate the counterdiabatic operator, in …