QArray+：一种基于物理信息、GPU加速的量子点阵列模拟器
QArray+: A physics-informed GPU-accelerated simulator for quantum dot arrays
- NVIDIA（英伟达）
- Department of Engineering Science, University of Oxford（牛津大学工程系）
- QuTech and Kavli Institute of Nanoscience, Delft University of Technology（代尔夫特理工大学 QuTech 和开尔文纳米科学研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
QArray+是基于物理信息的GPU加速量子点阵列模拟器，纳入栅极隧穿耦合与量子开放系统描述，可高效模拟相关动力学，为自动化器件调谐生成高通量数据集。
AI中文摘要：
半导体量子点阵列是可扩展量子技术的极具吸引力的平台，但其实际运行受到大规模器件调谐的复杂性阻碍。现有的自动化工具依赖简化的物理模型，如恒定电容近似和平衡哈伯德模型，这些模型假设能瞬时弛豫到稳态。这些框架在测量速率超过隧穿动力学的实验关键机制中失效，因此需要更复杂的非平衡控制策略。为了弥合这一差距，我们引入QArray+，它是QArray框架的扩展，纳入了依赖栅极的隧穿耦合和耗散过程的量子开放系统描述。该方法能够统一模拟相干的点间电荷态杂化，以及对训练用于自动化器件操作的鲁棒机器学习模型至关重要的非平衡锁存动力学。QArray+在JAX中实现，具备GPU加速，可在单个GPU和多节点系统上扩展。例如，在多个GPU上，可在约0.17秒内计算出64个点、100×100栅极电压网格的电荷稳定性图。由于点间相互作用是短程的，相应的调谐修正也是局部的，这些规模的模拟能够捕捉甚至更大器件的相关物理特性。这些能力支持为自动化器件调谐生成高通量数据集。
英文摘要：
Semiconductor quantum-dot arrays are a compelling platform for scalable quantum technologies, yet their practical operation is hindered by the complexity of tuning large-scale devices. Existing automation tools rely on simplified physical models---such as constant-capacitance approximations and equilibrium Hubbard models---which assume instantaneous relaxation to a steady state. These frameworks fail in experimentally critical regimes where measurement rates exceed tunneling dynamics, necessitating more sophisticated non-equilibrium control strategies. To bridge this gap, we introduce QArray+, an extension of the QArray framework that incorporates gate-dependent tunnel coupling and a quantum open-system description of dissipative processes. This approach enables the unified simulation of coherent interdot charge-state hybridization and the non-equilibrium latching dynamics essential for training robust machine-learning models for automated device operation. Implemented in JAX with GPU acceleration, QArray+ scales across GPUs and multi-node systems. For example, a charge stability diagram for a 100X100 grid of gate voltages over 64 dots can be computed in $\sim0.17\,\mathrm{s}$ on multiple GPUs. Since interdot interactions are short-ranged and the corresponding tuning corrections are local, simulations at these scales capture the physics relevant to even larger devices. These capabilities support high-throughput dataset generation for automated device tuning.
