Publications

updated 2023-05-01

Peer-reviewed

D. Iouchtchenko, J. F. Gonthier, A. Perdomo-Ortiz, and R. G. Melko. (2023) Neural network enhanced measurement efficiency for molecular groundstates. Machine Learning: Science and Technology 4, 015016. doi:10.1088/2632-2153/acb4df, arXiv:2206.15449, hdl:10012/19366

S. Mainali, F. Gatti, D. Iouchtchenko, P.-N. Roy, and H.-D. Meyer. (2021) Comparison of the multi-layer multi-configuration time-dependent Hartree (ML-MCTDH) method and the density matrix renormalization group (DMRG) for ground state properties of linear rotor chains. The Journal of Chemical Physics 154, 174106. doi:10.1063/5.0047090, hdl:10012/17126

I. J. S. De Vlugt, D. Iouchtchenko, E. Merali, P.-N. Roy, and R. G. Melko. (2020) Reconstructing quantum molecular rotor ground states. Physical Review B 102, 035108. doi:10.1103/PhysRevB.102.035108, arXiv:2003.14273, hdl:10012/16033
T. Sahoo, D. Iouchtchenko, C. M. Herdman, and P.-N. Roy. (2020) A path integral ground state replica trick approach for the computation of entanglement entropy of dipolar linear rotors. The Journal of Chemical Physics 152, 184113. doi:10.1063/5.0004602, hdl:10012/15910

N. Raymond, D. Iouchtchenko, P.-N. Roy, and M. Nooijen. (2018) A path integral methodology for obtaining thermodynamic properties of nonadiabatic systems using Gaussian mixture distributions. The Journal of Chemical Physics 148, 194110. doi:10.1063/1.5025058, arXiv:1805.05971, hdl:10012/13357
D. Iouchtchenko, and P.-N. Roy. (2018) Ground states of linear rotor chains via the density matrix renormalization group. The Journal of Chemical Physics 148, 134115. doi:10.1063/1.5024403, arXiv:1805.05420, hdl:10012/13362
T. Halverson, D. Iouchtchenko, and P.-N. Roy. (2018) Quantifying entanglement of rotor chains using basis truncation: Application to dipolar endofullerene peapods. The Journal of Chemical Physics 148, 074112. doi:10.1063/1.5011769, hdl:10012/13363

D. Iouchtchenko, and P.-N. Roy. (2016) Estimating Ground State Entanglement Entropy Using Path Integral Molecular Dynamics. Recent Progress in Quantum Monte Carlo. ACS Symposium Series, 1234. Chapter 10, pp. 145-154. Featured on book cover. doi:10.1021/bk-2016-1234.ch010
R. G. Melko, C. M. Herdman, D. Iouchtchenko, P.-N. Roy, and A. Del Maestro. (2016) Entangling qubit registers via many-body states of ultracold atoms. Physical Review A 93, 042336. doi:10.1103/PhysRevA.93.042336, arXiv:1512.06462, hdl:10012/13353

D. Iouchtchenko, K. P. Bishop, and P.-N. Roy. (2021) On the quantum mechanical potential of mean force. II. Constrained path integral molecular dynamics integrators. arXiv:2101.00762
D. Iouchtchenko, K. P. Bishop, and P.-N. Roy. (2021) On the quantum mechanical potential of mean force. I. A path integral perspective. arXiv:2101.00761

D. Iouchtchenko, N. Raymond, P.-N. Roy, and M. Nooijen. (2019) Deterministic and quasi-random sampling of optimized Gaussian mixture distributions for vibronic Monte Carlo. arXiv:1912.11594

D. Iouchtchenko. (2021) Algorithms for quantum molecular dynamics: from matrix product states to path integrals. University of Waterloo, PhD thesis. hdl:10012/16654
D. Iouchtchenko. (2015) Variations on PIGS: Non-standard approaches for imaginary-time path integrals. University of Waterloo, MSc thesis. hdl:10012/9559, GitHub:0/msc-thesis