Auto Quantum Machine Learning for Multisource Classification

Tomasz Rybotycki; Sebastian Dziura; Piotr Gawron

quant-phFeb 20, 2026

Auto Quantum Machine Learning for Multisource Classification

Authors: Tomasz Rybotycki, Sebastian Dziura, Piotr Gawron

Organizations: Center of Excellence in Artificial Intelligence, AGH University, al. Mickiewicza 30, 30-059 Cracow, Poland · Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences, ul. Bartycka 18, 00-716 Warsaw, Poland · Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland

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 & tables

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 $x_{\mathrm{bottom}}$ .

Parameter	Range	Type	Best value
Learning rate	$[10^{-3},\,10^{-2}]$	linear	$10^{-3}$
Batch size	$[32,128]$	discrete	$83$
Hidden layer size (feature extractors)	$[64,256]$	discrete	$90$
Output layer size (feature extractors)	$[64,256]$	discrete	$196$
Hidden layer size (classifier)	$[64,256]$	discrete	$95$

Table 1: Experiment setup for baseline hyperparameters optimization.

Parameter	Range	Type	Best value
PQC Manual ( AngleX $\to 3\times$ BEL )
Batch size	$[32,128]$	discrete	$32$
Hidden layer size (feature extractors)	$[64,256]$	discrete	$128$
Output layer size (feature extractors)	$[4,12]$	discrete	$6$
BasicEntangler layers number (classifier)	$[1,5]$	discrete	$3$
PQC ${}^{\mathrm{Solo}}_{\mathrm{AQML}}$ ( Amplitude $\to$ SEL $\to$ AngleY $\to 2\times$ SEL )

Table 2: Experiment setup for PQC hyperparameters optimization. For those experiments the learning rate was fixed to

lr=10^{-3}

. PQC

{}^{\mathrm{Solo}}_{\mathrm{AQML}}

and PQC

{}^{\mathrm{Linear}}_{\mathrm{AQML}}

denote PQC models without and with final classical linear layer respectively. We present the architecture of the model in the parenthesis, next to the model’s name.

Metric	Fold 1	Fold 2	Fold 3	Fold 4	Fold 5	Mean
MLP (# Parameters. Feature extractors: 38372; Classifier: 37623, Total: 75995)
accuracy	0.962	0.959	0.960	0.960	0.960	$0.960\pm 0.001$
f1 macro	0.961	0.959	0.959	0.959	0.959	$0.959\pm 0.001$
PQC manual (# Parameters. Feature extractors: 4614; Classifier: 18, Total: 4632)
accuracy	0.880	0.855	0.880	0.905	0.911	$0.886\pm 0.022$
f1 macro	0.879	0.852	0.879	0.903	0.910	$0.885\pm 0.023$

Table 3: Average accuracy and f1 macro -score metrics for classical, manual PQC and AQML-found PQC models. PQC

{}^{\mathrm{Solo}}_{\mathrm{AQML}}

and PQC

{}^{\mathrm{Linear}}_{\mathrm{AQML}}

denote PQC models without and with final classical linear layer respectively. We also provide trainable parameters number for each model.

Panel of Figure 4 — 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).

Quantum Machine Learning

Quantum machine learning uses quantum circuits and quantum data to build learning algorithms and study their computational and statistical limits.

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When do quantum views help? Complementary classical-quantum representations in multi-view fusion

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Hybrid Quantum-Classical ML Feature Fusion Multi-View Learning Quantum Feature Maps

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Recent studies have reported promising results from quantum machine learning in drug discovery, healthcare, materials science and cybersecurity. Research includes comparisons with classical machine learning algorithms and the development of sequential hybrid pipelines. We propose that fusion can improve prediction when classical and quantum representations capture complementary predictive relationships. Our classical-quantum multi-view framework preserves the full classical input alongside simulated quantum features. Concatenation and attention combine the views; single-view comparisons, replacement and masking controls assess both views' contributions. Controlled angular-polynomial experiments distinguish targets adequately represented by a single view from those that depend on complementary angular and polynomial relationships. For these mixed targets, fusion improves prediction by combining the relevant feature sets, while matched classical representations recover the same benefit, linking the improvement to their complementary predictive content. Controlled classification tests further show that gains depend on alignment between the chosen representation and the target, and on a suitable fusion rule. Benchmark validation shows improved mean predictive performance over classical baselines on Wine, Wisconsin Diagnostic Breast Cancer, three-class Fashion-MNIST and Steel Plates Faults within the evaluated protocols. Classical replacement and masking controls remain competitive, classical baselines lead on Digits and Covertype, and outcomes with missing or corrupted views depend on the available inputs. These findings support evaluating quantum representations for their complementary predictive contribution and matching the representation and fusion strategy to the target relationships.

Abstract

Quantum Machine Learning

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