QDFlow: A Python package for physics simulations of quantum dot devices

2025-09-16

AI Summary

High-quality labeled experimental data for quantum dot (QD) devices are scarce and costly to acquire. Method: We propose a physics-informed synthetic data generation framework that integrates a self-consistent Thomas–Fermi solver, a dynamic capacitance model, and a customizable noise module to produce high-fidelity charge stability diagrams and experiment-like ray diagrams—ensuring precise ground-truth labels, tunable physical parameters, and scalable dataset size. Contribution/Results: We develop QDFlow, an open-source Python-based simulation platform supporting calibration and control studies of multi-QD arrays. Our approach drastically reduces reliance on experimental labeling while enabling large-scale, physically grounded datasets for training, benchmarking, and validating machine learning models. This advances automated characterization and ML-driven control of quantum devices.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Recent advances in machine learning (ML) have accelerated progress in calibrating and operating quantum dot (QD) devices. However, most ML approaches rely on access to large, high-quality labeled datasets for training, benchmarking, and validation, with labels capturing key features in the data. Obtaining such datasets experimentally is challenging due to limited data availability and the labor-intensive nature of labeling. QDFlow is an open-source physics simulator for multi-QD arrays that generates realistic synthetic data with ground-truth labels. QDFlow combines a self-consistent Thomas-Fermi solver, a dynamic capacitance model, and flexible noise modules to produce charge stability diagrams and ray-based data closely resembling experiments. With extensive tunable parameters and customizable noise models, QDFlow supports the creation of large, diverse datasets for ML development, benchmarking, and quantum device research.

Problem

• Generating synthetic quantum dot data with ground-truth labels
• Addressing limited experimental data availability for ML training
• Providing realistic physics simulations for quantum device research

Innovation

• Physics simulator for quantum dot arrays
• Generates synthetic data with ground-truth labels
• Combines Thomas-Fermi solver with noise modules