用混合量子张量网络解决气动弹性问题，实现高精度时序分类与回归。
Hybrid quantum tensor networks for aeroelastic applications
- 结合张量网络与变分量子电路，构建端到端可训练模型
- 在二分类任务中达到高准确率，对离散变量回归也表现良好
- 适合研究量子机器学习在航空工程中的应用者参考
我们研究了混合量子张量网络在气动弹性问题中的应用，利用量子机器学习（QML）的优势。通过将张量网络与变分量子电路结合，展示了QML在处理复杂时间序列分类与回归任务中的潜力。结果表明，混合量子张量网络在二分类任务中具有高精度表现，同时在离散变量回归方面也展现出良好性能。尽管超参数选择仍具挑战性，需精细优化以发挥模型全部潜力，但本工作显著推动了QML在气动弹性复杂问题求解中的发展。我们提出一种端到端可训练的混合算法：首先将时间序列编码为张量网络，再通过可训练张量网络进行降维，并在编码步骤中将结果转换为量子电路；随后应用受张量网络启发的可训练变分量子电路，解决气动弹性领域的分类或多元/单变量回归任务。
原文摘要 · Abstract (English)
We investigate the application of hybrid quantum tensor networks to aeroelastic problems, harnessing the power of Quantum Machine Learning (QML). By combining tensor networks with variational quantum circuits, we demonstrate the potential of QML to tackle complex time series classification and regression tasks. Our results showcase the ability of hybrid quantum tensor networks to achieve high accuracy in binary classification. Furthermore, we observe promising performance in regressing discrete variables. While hyperparameter selection remains a challenge, requiring careful optimisation to unlock the full potential of these models, this work contributes significantly to the development of QML for solving intricate problems in aeroelasticity. We present an end-to-end trainable hybrid algorithm. We first encode time series into tensor networks to then utilise trainable tensor networks for dimensionality reduction, and convert the resulting tensor to a quantum circuit in the encoding step. Then, a tensor network inspired trainable variational quantum circuit is applied to solve either a classification or a multivariate or univariate regression task in the aeroelasticity domain.
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