A typical task for classical and quantum computing in chemistry is finding a potential energy surface (PES) along a reaction coordinate, which involves solving the quantum chemistry problem for many points along the reaction path. Developing algorithms to accomplish this task on quantum computers has been an active area of development, yet finding all the relevant eigenstates along the reaction coordinate remains a difficult problem, and determining PESs is thus a costly proposal. In this paper, we demonstrate the use of a eigenvector continuation -- a subspace expansion that uses a few eigenstates as a basis -- as a tool for rapidly exploring potential energy surfaces. We apply this to determining the binding PES or torsion PES for several...
Quantum simulation of quantum chemistry is one of the most compelling applications of quantum comput...
The variational quantum eigensolver (or VQE) uses the variational principle to compute the ground st...
Quantum computation is the most promising new paradigm for the simulation of physical systems compos...
A typical task for classical and quantum computing in chemistry is finding a potential energy surfac...
Quantum subspace diagonalization (QSD) methods are quantum-classical hybrid methods, commonly used t...
We develop a quantum-classical hybrid algorithm to calculate the analytical second-order derivative ...
In the noisy-intermediate-scale-quantum era, Variational Quantum Eigensolver (VQE) is a promising me...
This article is a brief introduction to quantum algorithms for the eigenvalue problem in quantum man...
Extracting eigenvalues and eigenvectors of exponentially large matrices will be an important applica...
Computational cost of energy estimation for molecular electronic Hamiltonians via Quantum Phase Esti...
We investigate the possibility to calculate the ground-state energy of the atomic systems on a quant...
We propose a simple procedure to produce energy eigenstates of a Hamiltonian with discrete eigenvalu...
Within the reduced basis methods approach, an effective low-dimensional subspace of a quantum many-b...
The exact evaluation of the molecular ground state in quantum chemistry requires an exponentially in...
We propose a nonvariational scheme for geometry optimization of molecules for the first-quantized ei...
Quantum simulation of quantum chemistry is one of the most compelling applications of quantum comput...
The variational quantum eigensolver (or VQE) uses the variational principle to compute the ground st...
Quantum computation is the most promising new paradigm for the simulation of physical systems compos...
A typical task for classical and quantum computing in chemistry is finding a potential energy surfac...
Quantum subspace diagonalization (QSD) methods are quantum-classical hybrid methods, commonly used t...
We develop a quantum-classical hybrid algorithm to calculate the analytical second-order derivative ...
In the noisy-intermediate-scale-quantum era, Variational Quantum Eigensolver (VQE) is a promising me...
This article is a brief introduction to quantum algorithms for the eigenvalue problem in quantum man...
Extracting eigenvalues and eigenvectors of exponentially large matrices will be an important applica...
Computational cost of energy estimation for molecular electronic Hamiltonians via Quantum Phase Esti...
We investigate the possibility to calculate the ground-state energy of the atomic systems on a quant...
We propose a simple procedure to produce energy eigenstates of a Hamiltonian with discrete eigenvalu...
Within the reduced basis methods approach, an effective low-dimensional subspace of a quantum many-b...
The exact evaluation of the molecular ground state in quantum chemistry requires an exponentially in...
We propose a nonvariational scheme for geometry optimization of molecules for the first-quantized ei...
Quantum simulation of quantum chemistry is one of the most compelling applications of quantum comput...
The variational quantum eigensolver (or VQE) uses the variational principle to compute the ground st...
Quantum computation is the most promising new paradigm for the simulation of physical systems compos...