QArray+: A physics-informed GPU-accelerated simulator for quantum dot arrays
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 ∼0.17 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.
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BibTeX
@misc{vaidhyanathan2026qarray,
title = {{QArray+: A physics-informed GPU-accelerated simulator for quantum dot arrays}},
author = {Pranav Vaidhyanathan and Barnaby van Straaten and Alice Petrillo and Rahul Marchand and Edwin De Nicolo and Menno Veldhorst and Brucek Khailany and Taylor L. Patti and Natalia Ares},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.02736},
eprint = {2609.02736},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.02736},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - QArray+: A physics-informed GPU-accelerated simulator for quantum dot arrays
AU - Pranav Vaidhyanathan
AU - Barnaby van Straaten
AU - Alice Petrillo
AU - Rahul Marchand
AU - Edwin De Nicolo
AU - Menno Veldhorst
AU - Brucek Khailany
AU - Taylor L. Patti
AU - Natalia Ares
PY - 2026
DA - 2026/09/02
PB - arXiv
DO - 10.48550/arxiv.2609.02736
UR - https://arxiv.org/abs/2609.02736
AB - 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 ∼0.17 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.
ER -