QArray+: A physics-informed GPU-accelerated simulator for quantum dot arrays

2026-09-02

AI Summary

为解决半导体量子点阵列调谐复杂性问题，通过引入QArray+模拟器，采用物理信息和GPU加速方法来实现非平衡控制策略。

Abstract

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.

Problem

• quantum dot arrays
• large-scale devices
• non-equilibrium dynamics
• automation tools
• tunneling dynamics

Innovation

• gate-dependent tunnel coupling
• quantum open-system description
• non-equilibrium dynamics
• GPU acceleration
• high-throughput dataset generation