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 computing is computing beyond classical computing based on quantum
phenomena such as superposition and entanglement. While quantum computing is still …

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 …

We benchmark the quantum processing units of the largest quantum annealers to date, the
5000+ qubit quantum annealer Advantage and its 2000+ qubit predecessor D-Wave 2000Q …

Quantum annealing (QA) and Quantum Alternating Operator Ansatz (QAOA) are both
heuristic quantum algorithms intended for sampling optimal solutions of combinatorial …

Quantum annealing offers an approach to finding the optimal solutions for a variety of
computational problems, where the quantum annealing controls influence the observed …

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 …

User demand for increasing amounts of wireless capacity continues to outpace supply, and
so to meet this demand, significant progress has been made in new MIMO wireless physical …