Jun Yang's research while affiliated with Dartmouth College and other places

Publications (5)

Preprint
A machine learning method to develop the energy functional and the Kohn-Sham potential of a time-dependent Kohn-Sham(TDKS) system is proposed. The method is based on the dynamics of the Kohn-Sham system, so no data of the exact Kohn-Sham potential is required for training the model. We demonstrate the results of our method with a 1d harmonic oscill...
Article
Full-text available
Quantum algorithms are touted as a way around some classically intractable problems such as the simulation of quantum mechanics. At the end of all quantum algorithms is a quantum measurement whereby classical data is extracted and utilized. In fact, many of the modern hybrid-classical approaches are essentially quantum measurements of states with s...
Article
One route to numerically propagating quantum systems is time-dependent density functional theory (TDDFT). The application of TDDFT to a particular system’s time evolution is predicated on V-representability which we have analyzed in a previous publication. Here we describe a newly developed solver for the scalar time-dependent Kohn-Sham potential....
Preprint
Full-text available
Quantum algorithms are touted as a way around some classically intractable problems such as the simulation of quantum mechanics. At the end of all quantum algorithms is a quantum measurement whereby classical data is extracted and utilized. In fact, many of the modern hybrid-classical approaches are essentially quantum measurements of states with s...
Preprint
One route to numerically propagating quantum systems is time-dependent density functional theory (TDDFT). The application of TDDFT to a particular system's time evolution is predicated on $V$-representability which we have analyzed in a previous publication. Here we describe a newly developed solver for the scalar time-dependent Kohn-Sham potential...

Citations

... The same methodology can be applied to both the groundstate and time-dependent reverse-engineering problems, however the former requires iterative solutions of the time-independent Kohn-Sham equations, whereas the latter requires repeated iterative application of the time-evolution operator to the Kohn-Sham states. A number of iterative algorithms have been proposed in recent literature to realise the inverse density-to-potential map in time-dependent and ground-state DFT [224][225][226][227][228][229]. For example, some of these approaches consider iterative schemes derived from the continuity equation of time-dependent DFT [225,226], whereas the original timedependent reverse-engineering algorithm in iDEA utilised iterative updates of the vector potential in the context of time-dependent current DFT (matching the current densities) [218]. ...