Quantum optical neural networks using atom-cavity interactions to provide all-optical nonlinearity
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914143944048640 |
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| author | Zhu, Chuanzhou Wang, Tianyu McMahon, Peter L. Soh, Daniel |
| author_facet | Zhu, Chuanzhou Wang, Tianyu McMahon, Peter L. Soh, Daniel |
| contents | Optical neural networks (ONNs) have been developed to enhance processing speed and energy efficiency in machine learning by leveraging optical devices for nonlinear activation and establishing connections among neurons. In this work, we propose a quantum optical neural network (QONN) that utilizes atom-cavity neurons with controllable photon absorption and emission. These quantum neurons are designed to replace the electronic components in ONNs, which typically introduce delays and substantial energy consumption during nonlinear activation. To evaluate the performance of the QONN, we apply it to the MNIST digit classification task, considering the effects of photon absorption duration, random atom-cavity detuning, and stochastic photon loss. Additionally, we introduce a convolutional QONN to facilitate a real-world satellite image classification (SAT-6) task. Due to its compact hardware and low power consumption, the QONN offers a promising solution for real-time satellite sensing, reducing communication bandwidth with ground stations and thereby enhancing data security. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06167 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum optical neural networks using atom-cavity interactions to provide all-optical nonlinearity Zhu, Chuanzhou Wang, Tianyu McMahon, Peter L. Soh, Daniel Quantum Physics Atomic Physics Optics Optical neural networks (ONNs) have been developed to enhance processing speed and energy efficiency in machine learning by leveraging optical devices for nonlinear activation and establishing connections among neurons. In this work, we propose a quantum optical neural network (QONN) that utilizes atom-cavity neurons with controllable photon absorption and emission. These quantum neurons are designed to replace the electronic components in ONNs, which typically introduce delays and substantial energy consumption during nonlinear activation. To evaluate the performance of the QONN, we apply it to the MNIST digit classification task, considering the effects of photon absorption duration, random atom-cavity detuning, and stochastic photon loss. Additionally, we introduce a convolutional QONN to facilitate a real-world satellite image classification (SAT-6) task. Due to its compact hardware and low power consumption, the QONN offers a promising solution for real-time satellite sensing, reducing communication bandwidth with ground stations and thereby enhancing data security. |
| title | Quantum optical neural networks using atom-cavity interactions to provide all-optical nonlinearity |
| topic | Quantum Physics Atomic Physics Optics |
| url | https://arxiv.org/abs/2511.06167 |