Auto Quantum Machine Learning for Multisource Classification

TL;DR

This work introduces an automated QML (AQML) approach for addressing data fusion challenges and evaluates how AQML-generated quantum circuits perform compared to classical multilayer perceptrons (MLPs) and manually designed QML models when processing multisource inputs.

Abstract

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.

Figures (4)

• Figure 1: A schematic representation of all the models used in this work: yellow --- feature extractor, blue --- classifier (here the fusion takes place), green --- optional output logits for quantum classifiers. XOR on the schematic means that only one type of classifier is used at the time. Top classifier represents MLP approach, and the bottom one parametrized quantum circuit approach.
• Figure 2: A general schematic of a parameterized quantum circuit. It consists of $K$ QML blocks, each containing a data (re-)uploading operation and a variational operation. Notice that both $U_{\mathrm{load}}$ and $U_{\mathrm{var}}$ could, in principle, be an identity. On the schematic, $x$ denotes the input, and $\theta$ denotes the full set of PQC parameters. The PQC is concluded with a measurement.
• Figure 3: Overview of the synthetic multisource preprocessing pipeline. (a) Original input image. (b) Horizontal splitting into top and bottom halves. (c) Binarization of each half. (d) Spatial downsampling. (e) Computation of column-wise mean pixel intensities, resulting in vector representations that constitute the synthetic multisource inputs $x_{\mathrm{top}}$ and $x_{\mathrm{bottom}}$.
• Figure 4: A pair of images from the ONERA dataset. They show Saclay, a city in France. The images were taken on 15 III 2016 (Left) and 29 VIII 2017 (Right).