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.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.

physics.chem-phCross-listedarXiv:2602.17158Proceedings of the National Academy of SciencesFeb 20, 20262 citations

Open Quantum Systems: A Stochastic Perspective

Heiko Appel

Lecture notes in physicsJan 1, 20121 citation

Stochastic Tensor Contraction for Efficient MP2 Exchange

Jiace Sun, Garnet Kin‐Lic Chan

Second-order Moller--Plesset perturbation theory (MP2) is one of the simplest correlated wave-function methods, but its conventional $O(N^5)$ cost limits its application to large systems. Stochastic tensor contraction (STC) has recently appeared as a general technique to evaluate high-order tensor contractions in quantum chemistry. Here, we apply STC to the exchange contribution of Laplace-transformed MP2, which is the source of $O(N^5)$ scaling in the formulation. The resulting STC exchange algorithm has an $O(N^3)$ deterministic setup cost and an $O(N^2)$ stochastic cost at fixed absolute error. The estimator is unbiased and provides a way to specify the target stochastic error by estimating the number of required samples before the full calculation. We implement the algorithm using a hybrid deterministic--stochastic evaluation strategy, with grouped index sampling, to reduce the computational prefactor. Over a range of benchmark molecules containing up to $\sim 7000$ basis functions, the stochastic exchange evaluation takes as little as $1/270$ of the time of a complete DF-MP2 calculation on the same system. Within the Laplace-transformed formulation, the $O(N^4)$ scaling direct contribution is thus the only significant cost. Our results further substantiate the power of STC to serve as a general tensor-contraction engine for quantum chemistry.Second-order Moller--Plesset perturbation theory (MP2) is one of the simplest correlated wave-function methods, but its conventional $O(N^5)$ cost limits its application to large systems. Stochastic tensor contraction (STC) has recently appeared as a general technique to evaluate high-order tensor contractions in quantum chemistry. Here, we apply STC to the exchange contribution of Laplace-transformed MP2, which is the source of $O(N^5)$ scaling in the formulation. The resulting STC exchange algorithm has an $O(N^3)$ deterministic setup cost and an $O(N^2)$ stochastic cost at fixed absolute error. The estimator is unbiased and provides a way to specify the target stochastic error by estimating the number of required samples before the full calculation. We implement the algorithm using a hybrid deterministic--stochastic evaluation strategy, with grouped index sampling, to reduce the computational prefactor. Over a range of benchmark molecules containing up to $\sim 7000$ basis functions, the stochastic exchange evaluation takes as little as $1/270$ of the time of a complete DF-MP2 calculation on the same system. Within the Laplace-transformed formulation, the $O(N^4)$ scaling direct contribution is thus the only significant cost. Our results further substantiate the power of STC to serve as a general tensor-contraction engine for quantum chemistry.

arXiv:2609.03168Sep 2, 20260 citations

DMRG for multicomponent quantum chemistry

Mikuláš Matoušek

One of the more modern directions in quantum chemistry, which is mostly unexplored, is using Hamiltonians which include not only the electrons of the molecule, but also other quantum particles. These are also termed multicomponent Hamiltonians. The Density Matrix Renormalization Group method has become one of the mainline methods in multireference quantum chemistry. In this work we describe a way to extend the DMRG algorithm to use multicomponent Hamiltonians. We start by a description of the DMRG algorithm, showcasing the usage of quantum chemical DMRG on multiple challenging systems. We include a brief description how to extend the DMRG algorithm to also treat dynamical correlation with the Adiabatic Connection method. We then describe the extension of DMRG to multicomponent Hamiltonians which has been implemented by the author of this work, and finally show how DMRG can be useful for polaritonic chemistry, where the molecule interacts with the quantized states of light in an optical cavity.One of the more modern directions in quantum chemistry, which is mostly unexplored, is using Hamiltonians which include not only the electrons of the molecule, but also other quantum particles. These are also termed multicomponent Hamiltonians. The Density Matrix Renormalization Group method has become one of the mainline methods in multireference quantum chemistry. In this work we describe a way to extend the DMRG algorithm to use multicomponent Hamiltonians. We start by a description of the DMRG algorithm, showcasing the usage of quantum chemical DMRG on multiple challenging systems. We include a brief description how to extend the DMRG algorithm to also treat dynamical correlation with the Adiabatic Connection method. We then describe the extension of DMRG to multicomponent Hamiltonians which has been implemented by the author of this work, and finally show how DMRG can be useful for polaritonic chemistry, where the molecule interacts with the quantized states of light in an optical cavity.

Jan 1, 20250 citations

Quantum systems with stochastic parameters

Marek Pechal

Jan 1, 20090 citations

Second-order Moller--Plesset perturbation theory (MP2) is one of the simplest correlated wave-function methods, but its conventional

O(N^5)

cost limits its application to large systems. Stochastic tensor contraction (STC) has recently appeared as a general technique to evaluate high-order tensor contractions in quantum chemistry. Here, we apply STC to the exchange contribution of Laplace-transformed MP2, which is the source of

O(N^5)

scaling in the formulation. The resulting STC exchange algorithm has an

O(N^3)

deterministic setup cost and an

O(N^2)

stochastic cost at fixed absolute error. The estimator is unbiased and provides a way to specify the target stochastic error by estimating the number of required samples before the full calculation. We implement the algorithm using a hybrid deterministic--stochastic evaluation strategy, with grouped index sampling, to reduce the computational prefactor. Over a range of benchmark molecules containing up to

\sim 7000

basis functions, the stochastic exchange evaluation takes as little as

1/270

of the time of a complete DF-MP2 calculation on the same system. Within the Laplace-transformed formulation, the

O(N^4)

scaling direct contribution is thus the only significant cost. Our results further substantiate the power of STC to serve as a general tensor-contraction engine for quantum chemistry.