aiqbench analysis
QArray+ introduces a physics-informed, GPU-accelerated simulator that bridges the gap between simplified equilibrium models and the non-equilibrium dynamics required for large-scale quantum dot arrays. By accurately modeling gate-dependent tunnel coupling and dissipative processes, this tool enables high-throughput dataset generation essential for training robust machine learning models in automated device tuning. The implementation leverages JAX to achieve rapid computation of charge stability diagrams across multi-node GPU systems, directly addressing scalability challenges in semiconductor quantum technologies.
QArray+: A physics-informed GPU-accelerated simulator for quantum dot arrays
arXiv:2609.02736v1 Announce Type: cross 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.
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.
P.V and B.v.S contributed equally to this work. 19 pages, 13 figures
Mesoscale and Nanoscale Physics (cond-mat.mes-hall); Distributed, Parallel, and Cluster Computing (cs.DC); Quantum Physics (quant-ph)
arXiv:2609.02736 [cond-mat.mes-hall]
(or arXiv:2609.02736v1 [cond-mat.mes-hall] for this version)
https://doi.org/10.48550/arXiv.2609.02736 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 305 words
