自动量子机器学习提升多源数据融合分类精度
Auto Quantum Machine Learning for Multisource Classification
- 用自动化方法生成量子电路处理多源输入数据
- 在ONERA数据集上实现比已有QML方法更高的变化检测准确率
- 适合关注量子计算与遥感结合的科研人员
随着容错量子计算的临近，越来越多研究关注将量子计算方法应用于遥感等数据密集型科学领域。量子机器学习（QML）已在这些高要求任务中展现出潜力。其中，量子数据融合——一个复杂的多源数据分析问题——近年来备受关注。本文提出一种自动化量子机器学习（AQML）方法，用于应对数据融合挑战。我们评估了AQML生成的量子电路在处理多源输入时的表现，对比了经典多层感知机（MLPs）和人工设计的QML模型。此外，我们将该方法应用于多光谱ONERA数据集的变化检测任务，取得优于此前报道的基于QML的方法的准确率。
原文摘要 · Abstract (English)
With fault-tolerant quantum computing on the horizon, there is growing interest in applying quantum computational methods to data-intensive scientific fields like remote sensing. Quantum machine learning (QML) has already demonstrated potential for such demanding tasks. One area of particular focus is quantum data fusion -- a complex data analysis problem that has attracted significant recent attention. In this work, we introduce an automated QML (AQML) approach for addressing data fusion challenges. We evaluate how AQML-generated quantum circuits perform compared to classical multilayer perceptrons (MLPs) and manually designed QML models when processing multisource inputs. Furthermore, we apply our method to change detection using the multispectral ONERA dataset, achieving improved accuracy over previously reported QML-based change detection results.
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