Virtual Gates Enabled by Digital Surrogate of Quantum Dot Devices

TL;DR

This paper introduces a graph-based digital surrogate simulator for quantum dot devices that uses deep learning to accelerate simulations, enabling efficient device characterization, virtual gate construction, and improved quantum technology development.

Contribution

A modular, deep learning-accelerated simulator that models quantum dot devices and constructs virtual gates, enhancing design and control processes.

Findings

Accurately estimates crosstalk effects between gate electrodes.

Constructs virtual gates validated against experimental data.

Accelerates simulation processes for quantum device development.

Abstract

Advances in quantum technologies are often limited by slow device characterization, complex tuning requirements, and scalability challenges. Spin qubits in electrostatically defined quantum dots provide a promising platform but are not exempt from these limitations. Simulations enhance our understanding of such devices, and in many cases, rapid feedback between measurements and simulations can guide the development of optimal design and control strategies. Here, we introduce a modular, graph-based simulator that acts as a digital surrogate for a semiconductor quantum dot device, where computationally expensive processes are accelerated using deep learning. We demonstrate its potential by estimating crosstalk effects between gate electrodes and applying these estimates to construct virtual gates in a quantum dot device. We validate our approach through comparison with experiments on a double quantum dot defined in a Ge/SiGe heterostructure. We envision that this simulation framework will advance semiconductor-based quantum technologies by enabling more efficient design, characterization, and control of complex devices.