Experimental Neuromorphic Computing Based on Quantum Memristor
- Author(s)
- Mirela Selimović, Iris Agresti, Michał Siemaszko, Joshua Morris, Borivoje Dakić, Riccardo Albiero, Andrea Crespi, Francesco Ceccarelli, Roberto Osellame, Magdalena Stobińska-Moretto, Philip Walther
- Abstract
Machine learning has recently developed novel approaches, mimicking the synapses of the human brain to achieve similarly efficient learning strategies. Such a method retains the universality of standard ones, 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 behaviors, 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 test our model on several tasks, widely recognized to benchmark the performance of (quantum) neuromorphic models. 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.
- Organisation(s)
- Quantum Science
- External organisation(s)
- University of Warsaw, Österreichische Akademie der Wissenschaften (ÖAW), QUBO Technology GmbH, Institute of Genetics and Biophysics "Adriano Buzzati-Traverso", CNR, Politecnico di Milano
- Journal
- PRX Quantum
- Volume
- 7
- No. of pages
- 33067
- ISSN
- 2691-3399
- DOI
- https://doi.org/10.48550/arXiv.2504.18694
- Publication date
- 09-2026
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 102040 Quantum computing, 103026 Quantum optics
- ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials, General Computer Science, Mathematical Physics, General Physics and Astronomy, Applied Mathematics, Electrical and Electronic Engineering
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/ac8f6b77-36dd-4a4f-8bc4-7abf078bc61c