Application of compressed sensing to the simulation of atomic systems
arXiv:1205.6485 · doi:10.1073/pnas.1209890109
Abstract
Compressed sensing is a method that allows a significant reduction in the number of samples required for accurate measurements in many applications in experimental sciences and engineering. In this work, we show that compressed sensing can also be used to speed up numerical simulations. We apply compressed sensing to extract information from the real-time simulation of atomic and molecular systems, including electronic and nuclear dynamics. We find that for the calculation of vibrational and optical spectra the total propagation time, and hence the computational cost, can be reduced by approximately a factor of five.
7 pages, 5 figures
References in corpus (4)
- Efficient formalism for large scale ab initio molecular dynamics based on time-dependent density functional theory
- A time-dependent density functional theory scheme for efficient calculations of dynamic (hyper)polarizabilities
- Quantum process tomography of molecular dimers from two-dimensional electronic spectroscopy I: General theory and application to homodimers
- Magnetic circular dichroism in real-time time-dependent density functional theory
Cited by in corpus (17)
- Real-space grids and the Octopus code as tools for the development of new simulation approaches for electronic systems
- Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra
- Superfast Line Spectral Estimation
- Real-space density functional theory on graphical processing units: computational approach and comparison to Gaussian basis set methods
- Bloch dynamics in lattices with long range hoppings
- Applications of the Generalised Langevin Equation: towards a realistic description of the baths
- Accelerating ultrafast spectroscopy with compressive sensing
- Atomic norm denoising with applications to line spectral estimation
- Digital quantum simulation of NMR experiments
- An efficient quantum algorithm for spectral estimation
- Effective quantum state reconstruction using compressive sensing in NMR quantum computing
- Two-dimensional spectroscopy of Rydberg gases
- Computation of 2-D spectra assisted by compressed sampling
- Granger Causality for Compressively Sensed Sparse Signals
- Continuous Compressed Sensing of Inelastic and Quasielastic Helium Atom Scattering Spectra
- Breathing dynamics of the Bose polaron in a species-selective harmonic trap
- A sparse-sampling approach for the fast computation of matrices: application to molecular vibrations