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 …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

This article summarizes technical advances contained in the fifth major release of the Q-
Chem quantum chemistry program package, covering developments since 2015. A …

We present a review of the Unitary Coupled Cluster (UCC) ansatz and related ansätze
which are used to variationally solve the electronic structure problem on quantum …

By averaging over atomic details, coarse-grained (CG) models provide profound
computational and conceptual advantages for studying soft materials. In particular, bottom …

Computational models are an essential tool for the design, characterization, and discovery
of novel materials. Computationally hard tasks in materials science stretch the limits of …

block2 is an open source framework to implement and perform density matrix
renormalization group and matrix product state algorithms. Out-of-the-box it supports the …

We propose a quantum-classical hybrid algorithm of the power method, here dubbed as the
quantum power method, to evaluate H^ n| ψ⟩ with quantum computers, where n is a non …

The idea to use quantum mechanical devices to simulate other quantum systems is
commonly ascribed to Feynman. Since the original suggestion, concrete proposals have …

There has been recent interest in the deployment of ab initio density matrix renormalization
group (DMRG) computations on high performance computing platforms. Here, we introduce …