Hybrid quantum-classical variational algorithms such as the variational quantum
eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA) are …

Variational quantum eigensolvers (VQEs) combine classical optimization with efficient cost
function evaluations on quantum computers. We propose a new approach to VQEs using the …

We present a hybrid classical-quantum algorithm to solve optimization problems in current
quantum computers, whose basic idea is to assist variational quantum eigensolvers (VQE) …

A variational quantum eigensolver (VQE) optimizes parametrized eigenstates of a
Hamiltonian on a quantum processor by updating parameters with a classical computer …

Variational algorithms for strongly correlated chemical and materials systems are one of the
most promising applications of near-term quantum computers. We present an extension to …

Extracting eigenvalues and eigenvectors of exponentially large matrices will be an important
application of near-term quantum computers. The variational quantum eigensolver (VQE) …

The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm for
finding the minimum eigenvalue of a Hamiltonian that involves the optimization of a …

The variational quantum eigensolver (VQE) is an attractive possible application of near-term
quantum computers. Originally, the aim of the VQE is to find a ground state for a given …

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 …

The variational quantum eigensolver (VQE), a variational algorithm to obtain an
approximated ground state of a given Hamiltonian, is an appealing application of near-term …