QuaResC
QUARESC QUANTUM MACHINE LEARNING USING RESERVOIR COMPUTING

  • I.P.: Miguel C. Soriano, Roberta Zambrini
  • Fecha de inicio: 1 de Junio de 2020
  • Fecha de final: 31 de Mayo de 2023

The QuaResC project engages in a new collaboration UIB and CSIC researchers at IFISC with the objective to address an interdisciplinary topic between artificial intelligence and quantum physics: quantum machine learning using reservoir computing. The PIs of QuaResC Roberta Zambrini (CSIC) and Miguel C. Soriano (UIB) recently have started to explore the possibility to join their efforts and experience in the new direction of quantum machine learning. The project aims at extending the reservoir computing paradigm into the quantum regime exploiting quantum networks as a specific realization of quantum machine learning. The goal of QuaResC is a systematic exploration of different implementations of quantum reservoir computing both with qubits and continuous variables to address their capability and to establish the possibility of a quantum advantage in the context of noisy intermediate-scale quantum computing (NISQ). The project is coordinated with two subprojects (1) Quantum reservoir computing and quantum complex systems and (2) Quantum reservoir computing and non-linear dynamical systems.
External collaborators include: Claudio Gallicchio (University of Pisa), Sabrina Maniscalco (University of Turku) and Valentina Parigi (Kastler–Brossel Laboratory).

Investigadores

  • Roberta Zambrini

    Roberta Zambrini

  • Stefano Longhi

    Stefano Longhi

  • Miguel C. Soriano

    Miguel C. Soriano

  • Gian Luca Giorgi

    Gian Luca Giorgi

  • Albert Cabot

    Albert Cabot

  • Marco Cattaneo

    Marco Cattaneo

  • Rodrigo Martínez

    Rodrigo Martínez

Publicaciones recientes

Dynamical phase transitions in quantum reservoir computing

Martínez-Peña, Rodrigo; Giorgi, Gian Luca; Nokkala, Johannes; Soriano, Miguel C.; Zambrini, Roberta
Physical Review Letters 127, 100502 (1-7) (2021)

High-Performance Reservoir Computing With Fluctuations in Linear Networks

Nokkala, Johannes; Martínez-Peña, Rodrigo; Zambrini, Roberta; Soriano, Miguel C.
IEEE Transactions on Neural Networks and Learning Systems , (2021)

Time-Delay Identification Using Multiscale Ordinal Quantifiers

Soriano, Miguel C.; Zunino, Luciano
Entropy 23, 969 (2021)

Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

Mujal, Pere; Martínez-Peña, Rodrigo; Nokkala, Johannes; García-Beni, Jorge; Giorgi, Gian Luca; Soriano, Miguel C.; Zambrini, Roberta
Advanced Quantum Technologies , 2100027 (2021)

Synchronization and Non-Markovianity in open quantum systems

Karpat, G; Yalçınkaya, İ; Çakmak, B; Giorgi, G. L.; Zambrini, R
Physical Review A 103, 062217 (1-10) (2021)

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