Getting started in computational quantum chemistry

Enrico Clementi, Giorgina Corongiou

Atanu Bhattacharya, Kalyan Dasgupta, Binoy Paine

In this paper, we present a computational chemistry project that demonstrates the quantum dynamics of a free particle, using both classical and quantum computing algorithms. This project can be used in a computational quantum chemistry course in which the instructor introduces quantum computing. Students write their own programs to simulate the simplest example of quantum dynamics by using both classical and quantum computing algorithms. Through this article, it is expected that students of chemistry will get a good introduction to the emerging field of quantum chemistry on a quantum computer .

Ajit J. Thakkar

Yasuaki Ito, Satoki Tsuji, Haruto Fujii +4 more

Computational quantum chemistry is a theoretical approach that utilizes the principles of quantum mechanics to perform computations on the electronic structure of molecules and atoms. This method enables the prediction of properties such as molecular structure, energy, electron orbitals, and electron density. In the field of the first principles, or ab initio, which relies solely on computation and not on experimental data, mathematical understanding allows even those without a background in chemistry to perform molecular orbital computations. The computation of quantum chemistry is computationally intensive, making them highly applicable in the field of high-performance computing. However, initiating quantum chemistry can be challenging for newcomers due to the numerous specialized terms and complex equations from quantum mechanics and chemistry. Therefore, this paper aims to provide a clear and accessible compilation of the fundamental and essential algorithm of the Hartree- Fock method, from theory to implementation. The goal is to offer the minimum required knowledge, in an approachable manner, serving as a starting point for those interested in computational quantum chemistry.

Jiace Sun, Garnet Kin-Lic Chan

Many computational methods in ab initio quantum chemistry are formulated in terms of high-order tensor contractions, whose cost determines the size of system that can be studied. We introduce stochastic tensor contraction to perform such operations with greatly reduced cost, and present its application to the gold-standard quantum chemistry method, coupled cluster theory with up to perturbative triples. For total energy errors more stringent than chemical accuracy, we reduce the computational scaling to that of mean-field theory, while starting to approach the mean-field absolute cost, thereby challenging the existing cost-to-accuracy landscape. Benchmarks against state-of-the-art local correlation approximations further show that we achieve an order-of-magnitude improvement in both total computation time and error, with significantly reduced sensitivity to system dimensionality and electron delocalization. We conclude that stochastic tensor contraction is a powerful computational primitive to accelerate a wide range of quantum chemistry.