activity
20142026
most citedDevelopments in Stochastic Coupled Cluster Theory: The initiator approximation and application to the Uniform Electron Gas

51 citations · 657 across the 43 of their papers we have counts for

collaborators
Showing 2023Show all

7 papers · 1 filter

quant-ph2023★ 1 cited

Optimised Baranyai partitioning of the second quantised Hamiltonian

Bence Csakany, Alex J. W. Thom

Simultaneous measurement of multiple Pauli strings (tensor products of Pauli matrices) is the basis for efficient measurement of observables on quantum computers by partitioning th…

physics.chem-ph2023★ 6 cited

Corrected Density Functional Theory and the Random Phase Approximation: Improved Accuracy at Little Extra Cost

Daniel Graf, Alex J. W. Thom

We recently introduced an efficient methodology to perform density-corrected Hartree-Fock density functional theory (DC(HF)-DFT) calculations and an extension to it we called "corr…

quant-ph2023★ 30 cited

Folded Spectrum VQE : A quantum computing method for the calculation of molecular excited states

Lila Cadi Tazi, Alex J. W. Thom

The recent developments of quantum computing present potential novel pathways for quantum chemistry, as the increased computational power of quantum computers could be harnessed to…

physics.chem-ph2023★ 10 cited

A simple and efficient route towards improved energetics within the framework of density-corrected density functional theory

Daniel Graf, Alex J. W. Thom

The crucial step in density-corrected Hartree-Fock density functional theory (DC(HF)-DFT) is to decide whether the density produced by the density functional for a specific calcula…

physics.chem-ph2023★ 7 cited

A hybrid stochastic configuration interaction-coupled cluster approach for multireference systems

Maria-Andreea Filip, Alex J. W. Thom

The development of multireference coupled cluster (MRCC) techniques has remained an open area of study in electronic structure theory for decades due to the inherent complexity of…

physics.chem-ph2023★ 3 cited

Global descriptors of a water molecule for machine learning of potential energy surfaces

Fabio E. A. Albertani, Alex J. W. Thom

Machine learning of multi-dimensional potential energy surfaces, from purely ab initio datasets, has seen substantial progress in the past years. Gaussian processes, a popular regr…