Comparing quantum and classical machine learning for radar-based drone classification: a like-for-like benchmark on noisy data

Comparing quantum and classical machine learning for radar-based drone classification: a like-for-like benchmark on noisy data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparing quantum and classical machine learning for radar-based drone classification: a like-for-like benchmark on noisy data Stefan Klug, Stefan Pickl, Maximilian Moll This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8437365/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Reliable radar-based classification of small unmanned aerial vehicles (UAVs, drones) is hampered by low signal-to-noise ratio (SNR), non-Gaussian clutter, and site-specific shifts. This motivates interest in Quantum Machine Learning (QML) as a potentially more noise-resilient alternative to classical methods, yet reproducible advantages over strong classical baselines remain unclear. This study presents a controlled, like-for-like comparison between feed-forward neural networks and pure QML models without a preceding classical dimensionality-reduction encoder. All models operate on the same binned Fourier features. The QML variants span two data encodings (angle, amplitude), two variational circuit families, and an optional Quantum Fourier Transform, and are trained either with Adam or the gradient-free Asexual Reproduction Optimization (ARO) under matched evaluation budgets.Across three regimes (noiseless, additive white Gaussian noise (AWGN), and colored AR(1) complex Gaussian noise (cAR(1)-GN)), QML achieves competitive accuracy with only tens of parameters and, in two settings (noiseless and AWGN), slightly exceeds the best classical baselines, although these differences are not statistically significant. Under temporally correlated cAR(1) Gaussian noise, the tuned classical network significantly outperforms the best QML configuration. Overall, QML provides parameter-efficient performance parity in two of three conditions but no definitive advantage. The results highlight a strong dependence on the noise regime and point to angle-encoded, ARO-trained circuits as the most promising QML candidates for noisy radar-based drone classification. quantum machine learning variational quantum classifiers quantum variational circuits micro-Doppler radar drone classification benchmarking Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 Mar, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers invited by journal 24 Feb, 2026 Editor assigned by journal 19 Feb, 2026 Submission checks completed at journal 23 Dec, 2025 First submitted to journal 23 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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