{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T05:44:12Z","timestamp":1762235052473,"version":"build-2065373602"},"reference-count":86,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad Nacional de Colombia","award":["Hermes-62836","Hermes 57661"],"award-info":[{"award-number":["Hermes-62836","Hermes 57661"]}]},{"DOI":"10.13039\/501100007626","name":"Universidad de Caldas","doi-asserted-by":"crossref","award":["Hermes-62836"],"award-info":[{"award-number":["Hermes-62836"]}],"id":[{"id":"10.13039\/501100007626","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Quantum machine learning (QML) integrates quantum computing with classical machine learning. Within this domain, QML-CQ classification tasks, where classical data is processed by quantum circuits, have attracted particular interest for their potential to exploit high-dimensional feature maps, entanglement-enabled correlations, and non-classical priors. Yet, practical realizations remain constrained by the Noisy Intermediate-Scale Quantum (NISQ) era, where limited qubit counts, gate errors, and coherence losses necessitate frugal, noise-aware strategies. The Data Re-Uploading (DRU) algorithm has emerged as a strong NISQ-compatible candidate, offering universal classification capabilities with minimal qubit requirements. While DRU has been experimentally demonstrated on ion-trap, photonic, and superconducting platforms, no implementations exist for spin-based quantum processing units (QPU-SBs), despite their scalability potential via CMOS-compatible fabrication and recent demonstrations of multi-qubit processors. Here, we present a pulse-level, noise-aware DRU framework for spin-based QPUs, designed to bridge the gap between gate-level models and realistic spin-qubit execution. Our approach includes (i) compiling DRU circuits into hardware-proximate, time-domain controls derived from the Loss\u2013DiVincenzo Hamiltonian, (ii) explicitly incorporating coherent and incoherent noise sources through pulse perturbations and Lindblad channels, (iii) enabling systematic noise-sensitivity studies across one-, two-, and four-spin configurations via continuous-time simulation, and (iv) developing a noise-aware training pipeline that benchmarks gate-level baselines against spin-level dynamics using information-theoretic loss functions. Numerical experiments show that our simulations reproduce gate-level dynamics with fidelities near unity while providing a richer error characterization under realistic noise. Moreover, divergence-based losses significantly enhance classification accuracy and robustness compared to fidelity-based metrics. Together, these results establish the proposed framework as a practical route for advancing DRU on spin-based platforms and motivate future work on error-attentive training and spin\u2013quantum-dot noise modeling.<\/jats:p>","DOI":"10.3390\/computers14110475","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T17:32:01Z","timestamp":1762191121000},"page":"475","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pulse-Driven Spin Paradigm for Noise-Aware Quantum Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Carlos","family":"Riascos-Moreno","sequence":"first","affiliation":[{"name":"Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0308-9576","authenticated-orcid":false,"given":"Andr\u00e9s Marino","family":"\u00c1lvarez-Meza","sequence":"additional","affiliation":[{"name":"Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0138-5489","authenticated-orcid":false,"given":"German","family":"Castellanos-Dominguez","sequence":"additional","affiliation":[{"name":"Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Elsayed, N., Maida, A.S., and Bayoumi, M. 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