23 citations · 63 across the 7 of their papers we have counts for
7 papers
Neural network enhanced measurement efficiency for molecular groundstates
Dmitri Iouchtchenko, Jérôme F. Gonthier, Alejandro Perdomo-Ortiz +1
It is believed that one of the first useful applications for a quantum computer will be the preparation of groundstates of molecular Hamiltonians. A crucial task involving state pr…
On the quantum mechanical potential of mean force. II. Constrained path integral molecular dynamics integrators
Dmitri Iouchtchenko, Kevin P. Bishop, Pierre-Nicholas Roy
Building on Paper I of this series, which introduced path integral Monte Carlo (PIMC) estimators for the derivative of the potential of mean force (PMF), we propose two path integr…
On the quantum mechanical potential of mean force. I. A path integral perspective
Dmitri Iouchtchenko, Kevin P. Bishop, Pierre-Nicholas Roy
We derive two path integral estimators for the derivative of the quantum mechanical potential of mean force (PMF), which may be numerically integrated to yield the PMF. For the fir…
Reconstructing quantum molecular rotor ground states
Isaac J. S. De Vlugt, Dmitri Iouchtchenko, Ejaaz Merali +2
Nanomolecular assemblies of C can be synthesized to enclose dipolar molecules. The low-temperature states of such endofullerenes are described by quantum mechanical rotors,…
Deterministic and quasi-random sampling of optimized Gaussian mixture distributions for vibronic Monte Carlo
Dmitri Iouchtchenko, Neil Raymond, Pierre-Nicholas Roy +1
It was recently shown that path integral Monte Carlo can be used to directly compute partition functions of Hamiltonians with vibronic coupling [J. Chem. Phys. 148, 194110 (2018)].…
Ground states of linear rotor chains via the density matrix renormalization group
Dmitri Iouchtchenko, Pierre-Nicholas Roy
In recent years, experimental techniques have enabled the creation of endofullerene peapod nanomolecular assemblies. It was previously suggested that the rotor model resulting from…
A path integral methodology for obtaining thermodynamic properties of nonadiabatic systems using Gaussian mixture distributions
Neil Raymond, Dmitri Iouchtchenko, Pierre-Nicholas Roy +1
We introduce a new path integral Monte Carlo method for investigating nonadiabatic systems in thermal equilibrium and demonstrate an approach to reducing stochastic error. We deriv…