https://doi.org/10.1140/epjqt/s40507-025-00352-3
Research
Quantum machine learning via continuous-variable cluster states and teleportation
1
Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC), UIB–CSIC, UIB Campus, E-07122, Palma de Mallorca, Spain
2
Laboratoire Kastler Brossel, Sorbonne Université, ENS-Université PSL, CNRS, Collège de France, 4 place Jussieu, 75252, Paris, France
a
jorgegarcia@ifisc.uib-csic.es
b
roberta@ifisc.uib-csic.es
Received:
6
December
2024
Accepted:
16
April
2025
Published online:
2
June
2025
We propose a new approach for a photonic platform suitable for distributed quantum machine learning and exhibiting memory. This measurement-based quantum reservoir computing takes advantage of continuous variable cluster states as the main quantum resource. Cluster states are key to several photonic quantum technologies, enabling universal quantum computing as well as quantum communication protocols. The proposed measurement-based quantum reservoir computing is based on a neural network of cluster states and local operations, where input data are encoded through measurement, thanks to quantum teleportation. In this design, measurements enable input injections, information processing and continuous monitoring for time series processing. The architecture’s power and versatility are tested by performing a set of benchmark tasks showing that the protocol displays internal memory and is suitable for both static and temporal information processing without hardware modifications. This design opens the way to distributed machine learning.
Key words: Quantum machine learning / Quantum reservoir computing / Cluster states / Quantum teleportation / Continuous-variable quantum optics / Quantum neural networks
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjqt/s40507-025-00352-3.
© The Author(s) 2025
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