Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

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  • Pere Mujal
    Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC) UIB–CSIC UIB Campus Palma de Mallorca E‐07122 Spain
  • Rodrigo Martínez‐Peña
    Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC) UIB–CSIC UIB Campus Palma de Mallorca E‐07122 Spain
  • Johannes Nokkala
    Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC) UIB–CSIC UIB Campus Palma de Mallorca E‐07122 Spain
  • Jorge García‐Beni
    Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC) UIB–CSIC UIB Campus Palma de Mallorca E‐07122 Spain
  • Gian Luca Giorgi
    Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC) UIB–CSIC UIB Campus Palma de Mallorca E‐07122 Spain
  • Miguel C. Soriano
    Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC) UIB–CSIC UIB Campus Palma de Mallorca E‐07122 Spain
  • Roberta Zambrini
    Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC) UIB–CSIC UIB Campus Palma de Mallorca E‐07122 Spain

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<jats:title>Abstract</jats:title><jats:p>Quantum reservoir computing and quantum extreme learning machines are two emerging approaches that have demonstrated their potential both in classical and quantum machine learning tasks. They exploit the quantumness of physical systems combined with an easy training strategy, achieving an excellent performance. The increasing interest in these unconventional computing approaches is fueled by the availability of diverse quantum platforms suitable for implementation and the theoretical progresses in the study of complex quantum systems. In this review article, recent proposals and first experiments displaying a broad range of possibilities are reviewed when quantum inputs, quantum physical substrates and quantum tasks are considered. The main focus is the performance of these approaches, on the advantages with respect to classical counterparts and opportunities.</jats:p>

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