TRAVOLTA: GPU acceleration and algorithmic improvements for constructing quantum optimal control fields in photo-excited systems

We present an open-source software package, TRAVOLTA (Terrific Refinements to Accelerate, Validate, and Optimize Large Time-dependent Algorithms), for carrying out massively parallelized quantum optimal control calculations on GPUs. The TRAVOLTA software package is a significant overhaul of our previous NIC-CAGE algorithm and also includes algorithmic improvements to the gradient ascent procedure to enable faster convergence. We examine three different variants of GPU parallelization to assess their performance in constructing optimal control fields in a variety of quantum systems. In addition, we provide several examples with extensive benchmarks of our GPU-enhanced TRAVOLTA code to show that it generates the same results as previous CPU-based algorithms but with a speedup that is more than ten times faster. Our GPU enhancements and algorithmic improvements enable large quantum optimal control calculations that can be efficiently and routinely executed on modern multi-core computational hardware.Program summaryProgram Title: TRAVOLTACPC Library link to program files: https://doi .org /10 .17632 /grwppm37rn .1Licensing provisions: GNU General Public License 3Programming language: C++, openBLAS, and CUDASupplementary material: Brief review of LU decomposition, raw numerical values used to generate Fig. 6 in the main text, and input examples for the TRAVOLTA software package.Nature of problem: The TRAVOLTA software package utilizes GPU accelerated routines and new algorithmic improvements to compute optimized electric fields that can drive a system from a known initial vibrational eigenstate to a specified final quantum state with a large (approximate to 1) transition probability.Solution method: Quantum control, GPU acceleration, analytic gradients, Crank-Nicolson propagation, and gradient ascent optimization.

Automated summary

This paper presents an open-source software package called TRAVOLTA for massively parallelized quantum optimal control calculations on GPUs. The TRAVOLTA package is an improvement on the previous NIC-CAGE algorithm and incorporates algorithmic improvements for faster convergence. Three different variants of GPU parallelization are examined to evaluate their performance in constructing optimal control fields in various quantum systems. The benchmarks show that the GPU-enhanced TRAVOLTA code produces the same results as previous CPU-based algorithms but with a speedup of more than ten times. The GPU enhancements and algorithmic improvements allow large quantum optimal control calculations to be efficiently executed on modern multi-core computational hardware.