A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …

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 …

Variational quantum machine learning is an extensively studied application of near-term
quantum computers. The success of variational quantum learning models crucially depends …

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 …

Abstract Variational Quantum Algorithms (VQAs) have received considerable attention due
to their potential for achieving near-term quantum advantage. However, more work is …

The quantum approximate optimization algorithm (QAOA) is a hybrid quantum-classical
variational algorithm designed to tackle combinatorial optimization problems. Despite its …

Hybrid classical–quantum algorithms aim to variationally solve optimization problems using
a feedback loop between a classical computer and a quantum co-processor, while …

Quantum variational algorithms are one of the most promising applications of near-term
quantum computers; however, recent studies have demonstrated that unless the variational …

The great promise of quantum computers comes with the dual challenges of building them
and finding their useful applications. We argue that these two challenges should be …