Publications

Differentiable quantum physics

Quantum Machine Learning

Fully differentiable optimization protocols for non-equilibrium steady states.
accepted in New J. Phys. (2021).
R. A. Vargas-Hernández, R. T. Q. Chen, K. A. Jung, and P. Brumer

Multi-objective optimization for retinal photoisomerization models with respect to experimental observables.
J. Chem. Phys. 155, 234109 (2021).
R. A. Vargas-Hernández, C. Chuang, and P. Brumer

Differentiable quantum computational chemistry with PennyLane.
arXiv:2111.09967 J. M. Arrazola et al.

Extrapolating quantum observables with machine learning: inferring multiple phase transitions from properties of a single phase.
Phys. Rev. Lett. 121, 255702 (2018).
R. A. Vargas-Hernández, J. Sous, M. Bercui, and R. V. Krems

Bayesian optimization for the inverse scattering problem in quantum reaction dynamics.
New J. Phys 21, 022001 (2019).
R. A. Vargas-Hernández, Y. Guan, D. H. Zhang, R. V. Krems
(Perspective) Work smarter, not harder: scientists use machine-learning algorithms to streamline quantum chemistry calculations. New J. Phys. 21, 022001 (2019). John L. Bohn

Inverse design of dissipative quantum steady-states with implicit differentiation.
Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020).
R. A. Vargas-Hernández, R. T. Q. Chen, K. A. Jung, and P. Brumer

Machine learning corrected quantum dynamics calculations.
Phys. Rev. Research 2, 032051(R) (2020).
A. Jasinski, J. Montaner, R. C. Forrey, B. H. Yang, P. C. Stancil, N. Balakrishnan, J. Dai, R. A. Vargas-Hernández, and R. V. Krems

Bayesian Optimization for Calibrating and Selecting Hybrid-Density Functional Models.
J. Phys. Chem. A 124, 4053 (2020).
R. A.Vargas-Hernández
Published as part of J. Phys. Chem. virtual special issue “Machine Learning in Physical Chemistry”.

Six-dimensional potential energy surface for NaK-NaK collisions: Gaussian Process representation with correct asymptotic form.
J. Chem. Phys. 150, 064106 (2019).
A. Christianen, T. Karman, R. A. Vargas-Hernández, G. C. Groenenboom, and Roman V. Krems

Assessing Gaussian Process Regression and Permutationally Invariant Polynomial Approaches to Represent High-Dimensional Potential Energy Surfaces.
J. Chem. Theory Comp. 14, 3381 (2018).
C. Qu, Q. Yu, B. L. Van Hoozen Jr., J. M. Bowman, and R. A. Vargas-Hernández

Neural Networks vs Gaussian Process Regression for Representing Potential Energy Surfaces: a Comparative Study of Fit Quality and Vibrational Spectrum Accuracy.
J. Chem. Phys. 148, 241702 (2017). (Editor’s picks)
A. Kamath, R. A. Vargas-Hernández, R. V. Krems, T. Carrington Jr., and S. Manzhos

Quantum Information

Quantum walks assisted by particle number fluctuations.
Phys. Rev. A 98, 022107 (2018).
R. A. Vargas-Hernández, and R. V. Krems

Quantum Simulators

Engineering extended Hubbard models with Zeeman excitations of ultracold Dy atoms.
J. Phys. B 49, 235501 (2016).
R. A. Vargas-Hernández, and R. V. Krems

Book chapters

Physical Extrapolation of Quantum Observables by Generalization with Gaussian Processes.
In: Schütt K., Chmiela S., von Lilienfeld O., Tkatchenko A., Tsuda K., Müller KR. (eds) Machine Learning Meets Quantum Physics. Lecture Notes in Physics, vol 968. Springer, Cham
R. A. Vargas-Hernández, and R. V. Krems

PhD Thesis

Applications of machine learning for solving complex quantum problems.
Ph.D. Dissertation, University of British Columbia, (2018).