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Experimental Neuromorphic Computing Based on Quantum Memristor

Mirela Selimović1,2,*, Iris Agresti1,†, Michał Siemaszko3, Joshua Morris1,2, Borivoje Dakić1,4,5, Riccardo Albiero6, Andrea Crespi7, Francesco Ceccarelli6, Roberto Osellame6 et al.

Magdalena Stobińska-Moretto8 and Philip Walther1,4,5,‡

  • 1University of Vienna, Faculty of Physics, Vienna Center for Quantum Science and Technology (VCQ), Boltzmanngasse 5, Vienna 1090, Austria
  • 2University of Vienna, Faculty of Physics, Vienna Doctoral School in Physics (VDSP), Boltzmanngasse 5, Vienna 1090, Austria
  • 3Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097 Warsaw, Poland
  • 4Institute for Quantum Optics and Quantum Information Sciences (IQOQI), Austrian Academy of Sciences, Boltzmanngasse 3, Vienna 1090, Austria
  • 5QUBO Technology GmbH, 1090 Vienna, Austria
  • 6Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche (IFN-CNR), piazza L. Da Vinci 32, 20133 Milano, Italy
  • 7Dipartimento di Fisica, Politecnico di Milano, piazza L. da Vinci 32, 20133 Milano, Italy
  • 8Center for Hybrid Quantum-Classical Information Technologies QLAB, University of Warsaw, Pasteura 5, 02-093 Warsaw, Poland
  • 9Faculty of Physics, University of Warsaw, Pasteura 5, 02-093 Warsaw, Poland

  • *Contact author: mirela.selmovic@univie.ac.at
  • †Contact author: iris.agresti@univie.ac.at
  • ‡Contact author: philip.walther@univie.ac.at
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PRX Quantum 7, 033067 – Published 25 September, 2026

DOI: https://doi.org/10.1103/jknv-3tx7

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.

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Popular Summary

Artificial intelligence is now part of everyday life, ranging from speech recognition to data analysis. Unfortunately, frontier systems are computationally expensive. Using light instead of electricity as the information carrier and exploiting quantum effects offer promising routes toward more sustainable technologies and enhanced performances, at least in specific tasks. Significant challenges remain though in how to achieve sufficient memory and flexible response to tackle complex tasks, possibly involving time-dependent input data. In this work, we demonstrate the first quantum machine learning model based on a quantum memristor: a photonic device that naturally combines memory and nonlinear behaviors, through an adaptive feedback mechanism, in addition to preserving quantum coherence of the inputs, enabling further quantum processing. We demonstrate that this model can successfully deal with time-series predictions and, through parallel units, can address real-world tasks such as speech recognition. Our results represent an important step toward fully optical machine learning protocols for both classical and quantum-native applications.

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PRX Quantum 7, 033067– Published 25 September, 2026
  • Received 1 July 2025
  • Revised 26 June 2026
  • Accepted 21 August 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

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