Descubriendo el potencial de la computación cuántica

@article{nokey,

title = {Quantum machine learning via continuous-variable cluster states and teleportation},

author = {García-Beni, J. and Paparelle, I. and Parigi, V. and Giorgi, G.L. and Soriano, M.C. and Zambrini, R.},

url = {https://epjquantumtechnology.springeropen.com/articles/10.1140/epjqt/s40507-025-00352-3},

doi = {doi.org/10.1140/epjqt/s40507-025-00352-3},

year  = {2025},

date = {2025-06-02},

journal = {EPJ Quantum Technology},

volume = {12},

number = {63},

issue = {1},

abstract = {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.},

keywords = {UIB},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Skin effect in quantum neural networks},

author = {Sannia, A. and Giorgi, G. L. and Longhi, S. and Zambrini, R. },

url = {https://opg.optica.org/opticaq/fulltext.cfm?uri=opticaq-3-2-189&id=569978},

doi = {doi.org/10.1364/OPTICAQ.541744},

year  = {2025},

date = {2025-04-04},

journal = {Optica Quantum },

volume = {3},

issue = {2},

pages = {189–194 },

abstract = {In the field of dissipative systems, the non-Hermitian skin effect has generated significant interest due to its unexpected implications. A system is said to exhibit a skin effect if its properties are largely affected by the boundary conditions. Despite the burgeoning interest, the potential impact of this phenomenon on emerging quantum technologies remains unexplored. In this work, we address this gap by demonstrating that quantum neural networks can exhibit this behavior and that skin effects, beyond their fundamental interest, can also be exploited in computational tasks. Specifically, we show that the performance of a given complex network used as a quantum reservoir computer is dictated solely by the boundary conditions of a dissipative line within its architecture. The closure of one (edge) link is found to drastically change the performance in time-series processing, proving the possibility of exploiting skin effects for machine learning.},

keywords = {UIB},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Role of coherence in many-body Quantum Reservoir Computing},

author = {Palacios, A. and Martínez-Pena, R. and Soriano, M.C. and Giorgi, G.L. and Zambrini, R. },

url = {https://www.nature.com/articles/s42005-024-01859-4},

doi = {doi.org/10.1038/s42005-024-01859-4},

year  = {2024},

date = {2024-11-14},

urldate = {2024-11-14},

journal = {Communications Physics},

volume = {7},

number = {369},

abstract = {Quantum Reservoir Computing (QRC) offers potential advantages over classical reservoir computing, including inherent processing of quantum inputs and a vast Hilbert space for state exploration. Yet, the relation between the performance of reservoirs based on complex and many-body quantum systems and non-classical state features is not established. Through an extensive analysis of QRC based on a transverse-field Ising model we show how different quantum effects, such as quantum

coherence and correlations, contribute to improving the performance in temporal tasks, as measured by the Information Processing Capacity. Additionally, we critically assess the impact of finite measurement resources and noise on the reservoir’s dynamics in different regimes, quantifying the limited ability to exploit quantum effects for increasing damping and noise strengths. Our results reveal a monotonic relationship between reservoir performance and coherence, along with the importance of quantum effects in the ergodic regime.},

keywords = {UIB},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Dissipation as a resource for Quantum Reservoir Computing},

author = {Sannia, A. and Martínez-Peña, R. and Soriano, M. and Giorgi, G.L. and Zambrini, R. },

url = {https://quantum-journal.org/papers/q-2024-03-20-1291/#},

doi = {doi.org/10.22331/q-2024-03-20-1291},

year  = {2024},

date = {2024-03-20},

journal = {Quantum },

volume = {8},

pages = {1291},

abstract = {Dissipation induced by interactions with an external environment typically hinders the performance of quantum computation, but in some cases can be turned out as a useful resource. We show the potential enhancement induced by dissipation in the field of quantum reservoir computing introducing tunable local losses in spin network models. Our approach based on continuous dissipation is able not only to reproduce the dynamics of previous proposals of quantum reservoir computing, based on discontinuous erasing maps but also to enhance their performance. Control of the damping rates is shown to boost popular machine learning temporal tasks as the capability to linearly and non-linearly process the input history and to forecast chaotic series. Finally, we formally prove that, under non-restrictive conditions, our dissipative models form a universal class for reservoir computing. It means that considering our approach, it is possible to approximate any fading memory map with arbitrary precision.},

keywords = {UIB},

pubstate = {published},

tppubtype = {article}

}