Pulsed learning for quantum data re-uploading models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911314088034304 |
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| author | Acedo, Ignacio B. Rodriguez-Grasa, Pablo Garcia-Azorin, Pablo Gonzalez-Conde, Javier |
| author_facet | Acedo, Ignacio B. Rodriguez-Grasa, Pablo Garcia-Azorin, Pablo Gonzalez-Conde, Javier |
| contents | While Quantum Machine Learning (QML) holds great potential, its practical realization on Noisy Intermediate-Scale Quantum (NISQ) hardware has been hindered by the limitations of variational quantum circuits (VQCs). Recent evidence suggests that VQCs suffer from severe trainability and noise-related issues, leading to growing skepticism about their long-term viability. However, the possibility of implementing learning models directly at the pulse-control level remains comparatively unexplored and could offer a promising alternative. In this work, we formulate a pulse-based variant of data re-uploading, embedding trainable parameters directly into the native system's dynamics. We benchmark our approach on a simulated superconducting transmon processor with realistic noise profiles. The pulse-based model consistently outperforms its gate-based counterpart, exhibiting higher test accuracy and improved generalization under equivalent noise conditions. Moreover, by systematically increasing noise strength, we show that pulse-level implementations retain higher fidelity for longer, demonstrating enhanced resilience to decoherence and control errors. These results suggest that pulse-native architectures, though less explored, may offer a viable and hardware-aligned path forward for practical QML in the NISQ era. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10670 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pulsed learning for quantum data re-uploading models Acedo, Ignacio B. Rodriguez-Grasa, Pablo Garcia-Azorin, Pablo Gonzalez-Conde, Javier Quantum Physics While Quantum Machine Learning (QML) holds great potential, its practical realization on Noisy Intermediate-Scale Quantum (NISQ) hardware has been hindered by the limitations of variational quantum circuits (VQCs). Recent evidence suggests that VQCs suffer from severe trainability and noise-related issues, leading to growing skepticism about their long-term viability. However, the possibility of implementing learning models directly at the pulse-control level remains comparatively unexplored and could offer a promising alternative. In this work, we formulate a pulse-based variant of data re-uploading, embedding trainable parameters directly into the native system's dynamics. We benchmark our approach on a simulated superconducting transmon processor with realistic noise profiles. The pulse-based model consistently outperforms its gate-based counterpart, exhibiting higher test accuracy and improved generalization under equivalent noise conditions. Moreover, by systematically increasing noise strength, we show that pulse-level implementations retain higher fidelity for longer, demonstrating enhanced resilience to decoherence and control errors. These results suggest that pulse-native architectures, though less explored, may offer a viable and hardware-aligned path forward for practical QML in the NISQ era. |
| title | Pulsed learning for quantum data re-uploading models |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2512.10670 |