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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2601.01399 |
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| _version_ | 1866915707427487744 |
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| author | Liu, Songyue Lu, Qi Zhong, Yuan Li, Yuru Xiang, Meng Li, Zhaohui Lu, Chao Su, Yikai Sun, Lu |
| author_facet | Liu, Songyue Lu, Qi Zhong, Yuan Li, Yuru Xiang, Meng Li, Zhaohui Lu, Chao Su, Yikai Sun, Lu |
| contents | Optical computing chips have emerged as a transformative computing technology due to their high computational density, low energy consumption, and compact footprint. While real- and complex-valued computing chips have been well developed, their fundamental limitations in representing high-dimensional data significantly constrain their applicability in modern signal processing. Quaternions enable direct operations on three- and four-dimensional data, powering high-dimensional processing in data analytics and artificial intelligence. Here we demonstrate a quaternion optical computing chip (QOCC) for the first time and benchmark its performance in several typical application scenarios: three-dimensional point cloud processing, RGB chromatic transformation, and quaternion convolutional neural network for color image recognition. The QOCC harnesses high parallelism of light by wavelength-division multiplexing, processing high-dimensional data simultaneously through multiple optical wavelength channels. Compared to the electronic computing counterpart, our QOCC achieves higher computational fidelity (root mean square error < 0.035) and substantially reduced computational load (2/3 lower). It paves the way towards next-generation optical computing, overcoming the limitations of traditional computing systems in high-dimensional data processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01399 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quaternion optical computing chip for parallel high-dimensional data processing Liu, Songyue Lu, Qi Zhong, Yuan Li, Yuru Xiang, Meng Li, Zhaohui Lu, Chao Su, Yikai Sun, Lu Optics Optical computing chips have emerged as a transformative computing technology due to their high computational density, low energy consumption, and compact footprint. While real- and complex-valued computing chips have been well developed, their fundamental limitations in representing high-dimensional data significantly constrain their applicability in modern signal processing. Quaternions enable direct operations on three- and four-dimensional data, powering high-dimensional processing in data analytics and artificial intelligence. Here we demonstrate a quaternion optical computing chip (QOCC) for the first time and benchmark its performance in several typical application scenarios: three-dimensional point cloud processing, RGB chromatic transformation, and quaternion convolutional neural network for color image recognition. The QOCC harnesses high parallelism of light by wavelength-division multiplexing, processing high-dimensional data simultaneously through multiple optical wavelength channels. Compared to the electronic computing counterpart, our QOCC achieves higher computational fidelity (root mean square error < 0.035) and substantially reduced computational load (2/3 lower). It paves the way towards next-generation optical computing, overcoming the limitations of traditional computing systems in high-dimensional data processing. |
| title | Quaternion optical computing chip for parallel high-dimensional data processing |
| topic | Optics |
| url | https://arxiv.org/abs/2601.01399 |