Applications such as simulating complicated quantum systems or solving large-scale linear
algebra problems are very challenging for classical computers, owing to the extremely high …

Abstract The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising
variational quantum algorithm that aims to solve combinatorial optimization problems that …

Abstract Variational Quantum Algorithms (VQAs) may be a path to quantum advantage on
Noisy Intermediate-Scale Quantum (NISQ) computers. A natural question is whether noise …

Data representation is crucial for the success of machine-learning models. In the context of
quantum machine learning with near-term quantum computers, equally important …

The quantum approximate optimization algorithm (QAOA) generates an approximate
solution to combinatorial optimization problems using a variational ansatz circuit defined by …

Combinatorial optimization on near-term quantum devices is a promising path to
demonstrating quantum advantage. However, the capabilities of these devices are …

Abstract Variational Quantum Algorithms (VQAs) are often viewed as the best hope for near-
term quantum advantage. However, recent studies have shown that noise can severely limit …

Airlines today are faced with a number of large-scale scheduling problems. One such
problem is the tail-assignment problem, which is the task of assigning individual aircraft to a …

We study the distribution over measurement outcomes of noisy random quantum circuits in
the regime of low fidelity, which corresponds to the setting where the computation …

The quantum approximate optimization algorithm (QAOA) is a promising quantum-classical
hybrid algorithm to solve hard combinatorial optimization problems. The multi-qubit …