The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …

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

We propose a quantum algorithm to solve systems of nonlinear differential equations. Using
a quantum feature map encoding, we define functions as expectation values of parametrized …

Abstract The study of Variational Quantum Eigensolvers (VQEs) has been in the spotlight in
recent times as they may lead to real-world applications of near-term quantum devices …

Optimization is one of the research areas where quantum computing could bring significant
benefits. In this scenario, a hybrid quantum–classical variational algorithm, the Quantum …

Quantum neural networks (QNNs) offer a powerful paradigm for programming near-term
quantum computers and have the potential to speed up applications ranging from data …

Abstract Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …

Quantum architecture search (QAS) is the process of automating architecture engineering of
quantum circuits. It has been desired to construct a powerful and general QAS platform …

Quantum computers are next-generation devices that hold promise to perform calculations
beyond the reach of classical computers. A leading method towards achieving this goal is …