Data underlying the publication: Transformer Models for Quantum Gate Set Tomography

This repository hosts the accompanying software for the following research article.<br>Research article: Transformer Models for Quantum Gate Set TomographyAbstract:Quantum computation represents a promising frontier in the domain of high-performance computing, blending quantum information theory with practical applications to overcome the limitations of classical computation. This study investigates the challenges of manufacturing high-fidelity and scalable quantum processors. Quantum gate set tomography (QGST) is a critical method for characterizing quantum processors and understanding their operational capabilities and limitations. This paper introduces ML4QGST as a novel approach to QGST by integrating machine learning techniques, specifically utilizing a transformer neural network model. Adapting the transformer model for QGST addresses the computational complexity of modeling quantum systems. Advanced training strategies, including data grouping and curriculum learning, are employed to enhance model performance, demonstrating significant congruence with ground-truth values. We benchmark this training pipeline on the constructed learning model, to successfully perform QGST for 3 gates on a 1 qubit system with over-rotation error and depolarizing noise estimation with comparable accuracy to pyGSTi. This research marks a pioneering step in applying deep neural networks to the complex problem of quantum gate set tomography, showcasing the potential of machine learning to tackle nonlinear tomography challenges in quantum computing.<br>Citation:@article{yu2024transformer, title={Transformer Models for Quantum Gate Set Tomography}, author={Yu, King Yiu and Sarkar, Aritra and Ishihara, Ryoichi and Feld, Sebastian}, journal={arXiv preprint arXiv:2405.02097}, year={2024} }<br>

本仓库配套提供下述研究论文的相关软件。<br>研究论文：用于量子门集层析成像的Transformer模型<br>摘要：量子计算作为高性能计算领域极具前景的前沿方向，融合量子信息理论与实际应用，旨在突破经典计算的局限性。本研究针对高保真度、可扩展量子处理器的制备挑战展开探索。量子门集层析成像（Quantum Gate Set Tomography, QGST）是表征量子处理器、明晰其运行性能与局限性的核心方法。本文提出ML4QGST作为量子门集层析成像的全新方法，通过融合机器学习技术，具体采用Transformer神经网络模型。将Transformer模型适配至量子门集层析成像任务，可有效解决量子系统建模中的计算复杂度难题。研究采用数据分组、课程学习等先进训练策略以优化模型性能，实验结果表明模型预测值与基准真值高度吻合。本研究基于所构建的学习模型对该训练流程开展基准测试，成功完成单量子比特系统中3个量子门的量子门集层析成像任务，同时完成过旋转误差与退极化噪声估计，其精度可与pyGSTi比肩。本研究首次将深度神经网络应用于量子门集层析成像这一复杂课题，展现了机器学习解决量子计算中非线性层析成像难题的潜力。<br>引用：@article{yu2024transformer, title="用于量子门集层析成像的Transformer模型", author={Yu, King Yiu and Sarkar, Aritra and Ishihara, Ryoichi and Feld, Sebastian}, journal={arXiv预印本 arXiv:2405.02097}, year={2024} }