VAN-DAMME: GPU-accelerated and symmetry-assisted quantum optimal control of multi-qubit systems
Year: 2024
Type: article
Abstract: We present an open-source software package, VAN-DAMME (Versatile Approaches to Numerically Design, Accelerate, and Manipulate Magnetic Excitations), for massively-parallelized quantum optimal control (QOC) calculations of multi-qubit systems. To enable large QOC calculations, the VAN-DAMME software package utilizes symmetry-based techniques with custom GPU-enhanced algorithms. This combined approach allows for the simultaneous computation of hundreds of matrix exponential propagators that efficiently leverage the intra-GPU parallelism found in high-performance GPUs. In addition, to maximize the computational efficiency of the VAN-DAMME code, we carried out several extensive tests on data layout, computational complexity, memory requirements, and performance. These extensive analyses allowed us to develop computationally efficient approaches for evaluating complex-valued matrix exponential propagators based on Padé approximants. To assess the computational performance of our GPU-accelerated VAN-DAMME code, we carried out QOC calculations of systems containing 10 - 15 qubits, which showed that our GPU implementation is 18.4× faster than the corresponding CPU implementation. Our GPU-accelerated enhancements allow efficient calculations of multi-qubit systems, which can be used for the efficient implementation of QOC applications across multiple domains. Program summary Program Title: VAN-DAMME CPC Library link to program files:: https://doi.org/10.17632/zcgw2n5bjf.1 Licensing provisions: GNU General Public License 3 Programming language: C++ and CUDA Nature of problem: The VAN-DAMME software package utilizes GPU-accelerated routines and new algorithmic improvements to compute optimized time-dependent magnetic fields that can drive a system from a known initial qubit configuration to a specified target state with a large (≈1) transition probability . Solution method: Quantum control , GPU acceleration, analytic gradients, matrix exponential, and gradient ascent optimization.
Source: Computer Physics Communications
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,410
Awards: G1079818582, G1143651532, G122673668, G1286236842, G1677143136, G1799333409, G2138846250, G3083819904, G419881185, G4280833397, G42916606, G4501827968, G498139845, G5076365615, G7354628648, DECODE: DATA-DRIVEN EXASCALE CONTROL OF OPTICALLY DRIVEN EXCITATIONS IN CHEMICAL AND MATERIAL SYSTEMS, G969889393
OpenAlex ID: W4403501593