Experimental neuromorphic computing based on quantum memristor
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908429177585664 |
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| author | Selimović, Mirela Agresti, Iris Siemaszko, Michał Morris, Joshua Dakić, Borivoje Albiero, Riccardo Crespi, Andrea Ceccarelli, Francesco Osellame, Roberto Stobińska, Magdalena Walther, Philip |
| author_facet | Selimović, Mirela Agresti, Iris Siemaszko, Michał Morris, Joshua Dakić, Borivoje Albiero, Riccardo Crespi, Andrea Ceccarelli, Francesco Osellame, Roberto Stobińska, Magdalena Walther, Philip |
| contents | Machine learning has recently developed novel approaches, mimicking the synapses of the human brain to achieve similarly efficient learning strategies. Such an approach retains the universality of standard methods, while attempting to circumvent their excessive requirements, which hinder their scalability. In this landscape, quantum (or quantum inspired) algorithms may bring enhancement. However, high-performing neural networks invariably display nonlinear behaviours, which poses a challenge to quantum platforms, given the intrinsically linear evolution of closed systems. We propose a strategy to enhance the nonlinearity achievable in this context, without resorting to entangling gates and report the first neuromorphic architecture based on a photonic quantum memristor. In detail, we show how the memristive feedback loop enhances the nonlinearity and hence the performance of the tested algorithms. We benchmark our model on four tasks, a nonlinear function and three time series prediction. In these cases, we highlight the essential role of the quantum memristive element and demonstrate the possibility of using it as a building block in more sophisticated networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18694 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Experimental neuromorphic computing based on quantum memristor Selimović, Mirela Agresti, Iris Siemaszko, Michał Morris, Joshua Dakić, Borivoje Albiero, Riccardo Crespi, Andrea Ceccarelli, Francesco Osellame, Roberto Stobińska, Magdalena Walther, Philip Quantum Physics Machine learning has recently developed novel approaches, mimicking the synapses of the human brain to achieve similarly efficient learning strategies. Such an approach retains the universality of standard methods, while attempting to circumvent their excessive requirements, which hinder their scalability. In this landscape, quantum (or quantum inspired) algorithms may bring enhancement. However, high-performing neural networks invariably display nonlinear behaviours, which poses a challenge to quantum platforms, given the intrinsically linear evolution of closed systems. We propose a strategy to enhance the nonlinearity achievable in this context, without resorting to entangling gates and report the first neuromorphic architecture based on a photonic quantum memristor. In detail, we show how the memristive feedback loop enhances the nonlinearity and hence the performance of the tested algorithms. We benchmark our model on four tasks, a nonlinear function and three time series prediction. In these cases, we highlight the essential role of the quantum memristive element and demonstrate the possibility of using it as a building block in more sophisticated networks. |
| title | Experimental neuromorphic computing based on quantum memristor |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2504.18694 |