VAN-DAMME: GPU-accelerated and symmetry-assisted quantum optimal control of multi-qubit systems

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.

本研究提出一款开源软件包VAN-DAMME（全称为Versatile Approaches to Numerically Design, Accelerate, and Manipulate Magnetic Excitations，即通用化数值设计、加速与操控磁激发方法），用于多量子比特系统的大规模并行量子最优控制（Quantum Optimal Control, QOC）计算。为支撑大规模QOC计算，VAN-DAMME软件包采用基于对称性的技术结合定制化GPU加速算法。该联合方案可同时计算数百个矩阵指数传播子，能够充分利用高性能GPU的内部并行计算特性。此外，为最大化VAN-DAMME代码的计算效率，本研究针对数据布局、计算复杂度、内存需求与运行性能开展了多组全面测试。通过上述分析，本研究开发出基于帕德逼近（Padé approximant）的复值矩阵指数传播子高效计算方法。为评估GPU加速版VAN-DAMME代码的计算性能，本研究针对包含10至15个量子比特的系统开展了QOC计算，结果表明该GPU实现版本的运行速度相较对应CPU实现版本快18.4倍。本研究的GPU加速优化方案可实现多量子比特系统的高效计算，能够支撑跨多领域的QOC应用高效部署。