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 …

The original variational quantum eigensolver (VQE) typically minimizes energy with hybrid
quantum-classical optimization that aims to find the ground state. Here, we propose a VQE …

The variational quantum eigensolver (VQE) is a promising algorithm to compute eigenstates
and eigenenergies of a given quantum system that can be performed on a near-term …

The variational quantum eigensolver (VQE) and its variants, which is a method for finding
eigenstates and eigenenergies of a given Hamiltonian, are appealing applications of near …

Variational quantum eigensolver (VQE) is a promising method for ground-state calculations
on current noisy intermediate-scale quantum computers. However, the research progress of …

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 …

Variational quantum algorithms (VQAs) are expected to establish valuable applications on
near-term quantum computers. However, recent works have pointed out that the …

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 …

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 …