Experimental quantum-enhanced kernels on a photonic processor
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914891904843776 |
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| author | Yin, Zhenghao Agresti, Iris de Felice, Giovanni Brown, Douglas Toumi, Alexis Pentangelo, Ciro Piacentini, Simone Crespi, Andrea Ceccarelli, Francesco Osellame, Roberto Coecke, Bob Walther, Philip |
| author_facet | Yin, Zhenghao Agresti, Iris de Felice, Giovanni Brown, Douglas Toumi, Alexis Pentangelo, Ciro Piacentini, Simone Crespi, Andrea Ceccarelli, Francesco Osellame, Roberto Coecke, Bob Walther, Philip |
| contents | Recently, machine learning had a remarkable impact, from scientific to everyday-life applications. However, complex tasks often imply unfeasible energy and computational power consumption. Quantum computation might lower such requirements, although it is unclear whether enhancements are reachable by current technologies. Here, we demonstrate a kernel method on a photonic integrated processor to perform a binary classification. We show that our protocol outperforms state-of-the-art kernel methods including gaussian and neural tangent kernels, exploiting quantum interference, and brings a smaller improvement also by single photon coherence. Our scheme does not require entangling gates and can modify the system dimension through additional modes and injected photons. This result opens to more efficient algorithms and to formulating tasks where quantum effects improve standard methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20364 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Experimental quantum-enhanced kernels on a photonic processor Yin, Zhenghao Agresti, Iris de Felice, Giovanni Brown, Douglas Toumi, Alexis Pentangelo, Ciro Piacentini, Simone Crespi, Andrea Ceccarelli, Francesco Osellame, Roberto Coecke, Bob Walther, Philip Quantum Physics Recently, machine learning had a remarkable impact, from scientific to everyday-life applications. However, complex tasks often imply unfeasible energy and computational power consumption. Quantum computation might lower such requirements, although it is unclear whether enhancements are reachable by current technologies. Here, we demonstrate a kernel method on a photonic integrated processor to perform a binary classification. We show that our protocol outperforms state-of-the-art kernel methods including gaussian and neural tangent kernels, exploiting quantum interference, and brings a smaller improvement also by single photon coherence. Our scheme does not require entangling gates and can modify the system dimension through additional modes and injected photons. This result opens to more efficient algorithms and to formulating tasks where quantum effects improve standard methods. |
| title | Experimental quantum-enhanced kernels on a photonic processor |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2407.20364 |