Quantum process tomography of structured optical gates with convolutional neural networks
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916951228416000 |
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| author | Jaouni, Tareq Di Colandrea, Francesco Amato, Lorenzo Cardano, Filippo Karimi, Ebrahim |
| author_facet | Jaouni, Tareq Di Colandrea, Francesco Amato, Lorenzo Cardano, Filippo Karimi, Ebrahim |
| contents | The characterization of a unitary gate is experimentally accomplished via Quantum Process Tomography, which combines the outcomes of different projective measurements to reconstruct the underlying operator. The process matrix is typically extracted from maximum-likelihood estimation. Recently, optimization strategies based on evolutionary and machine-learning techniques have been proposed. Here, we investigate a deep-learning approach that allows for fast and accurate reconstructions of space-dependent SU(2) operators, only processing a minimal set of measurements. We train a convolutional neural network based on a scalable U-Net architecture to process entire experimental images in parallel. Synthetic processes are reconstructed with average fidelity above 90%. The performance of our routine is experimentally validated on complex polarization transformations. Our approach further expands the toolbox of data-driven approaches to Quantum Process Tomography and shows promise in the real-time characterization of complex optical gates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16616 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum process tomography of structured optical gates with convolutional neural networks Jaouni, Tareq Di Colandrea, Francesco Amato, Lorenzo Cardano, Filippo Karimi, Ebrahim Quantum Physics The characterization of a unitary gate is experimentally accomplished via Quantum Process Tomography, which combines the outcomes of different projective measurements to reconstruct the underlying operator. The process matrix is typically extracted from maximum-likelihood estimation. Recently, optimization strategies based on evolutionary and machine-learning techniques have been proposed. Here, we investigate a deep-learning approach that allows for fast and accurate reconstructions of space-dependent SU(2) operators, only processing a minimal set of measurements. We train a convolutional neural network based on a scalable U-Net architecture to process entire experimental images in parallel. Synthetic processes are reconstructed with average fidelity above 90%. The performance of our routine is experimentally validated on complex polarization transformations. Our approach further expands the toolbox of data-driven approaches to Quantum Process Tomography and shows promise in the real-time characterization of complex optical gates. |
| title | Quantum process tomography of structured optical gates with convolutional neural networks |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2402.16616 |