On the Design of Expressive and Trainable Pulse-based Quantum Machine Learning Models
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908637186752512 |
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| author | Tao, Han-Xiao Wang, Xin Wu, Re-Bing |
| author_facet | Tao, Han-Xiao Wang, Xin Wu, Re-Bing |
| contents | Pulse-based Quantum Machine Learning (QML) has emerged as a novel paradigm in quantum artificial intelligence due to its exceptional hardware efficiency. For practical applications, pulse-based models must be both expressive and trainable. Previous studies suggest that pulse-based models under dynamic symmetry can be effectively trained, thanks to a favorable loss landscape that avoids barren plateaus. However, the resulting uncontrollability may compromise expressivity when the model is inadequately designed. This paper investigates the requirements for pulse-based QML models to be expressive while preserving trainability. We establish a necessary condition pertaining to the system's initial state, the measurement observable, and the underlying dynamical symmetry Lie algebra, supported by numerical simulations. Our findings provide a framework for designing practical pulse-based QML models that balance expressivity and trainability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05559 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Design of Expressive and Trainable Pulse-based Quantum Machine Learning Models Tao, Han-Xiao Wang, Xin Wu, Re-Bing Quantum Physics Machine Learning Pulse-based Quantum Machine Learning (QML) has emerged as a novel paradigm in quantum artificial intelligence due to its exceptional hardware efficiency. For practical applications, pulse-based models must be both expressive and trainable. Previous studies suggest that pulse-based models under dynamic symmetry can be effectively trained, thanks to a favorable loss landscape that avoids barren plateaus. However, the resulting uncontrollability may compromise expressivity when the model is inadequately designed. This paper investigates the requirements for pulse-based QML models to be expressive while preserving trainability. We establish a necessary condition pertaining to the system's initial state, the measurement observable, and the underlying dynamical symmetry Lie algebra, supported by numerical simulations. Our findings provide a framework for designing practical pulse-based QML models that balance expressivity and trainability. |
| title | On the Design of Expressive and Trainable Pulse-based Quantum Machine Learning Models |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2508.05559 |