Analytical evidence of nonlinearity in qubits and continuous-variable quantum reservoir computing
| Autor: | Roberta Zambrini, Johannes Nokkala, Miguel C. Soriano, Rodrigo Martínez-Peña, Pere Mujal, Gian Luca Giorgi |
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| Přispěvatelé: | Agencia Estatal de Investigación (España), Govern de les Illes Balears, Ministerio de Ciencia e Innovación (España), European Commission, Universidad de Las Islas Baleares, Ministerio de Economía y Competitividad (España), Ministerio de Educación y Formación Profesional (España) |
| Rok vydání: | 2021 |
| Předmět: |
Quantum reservoir computing
Nonlinearity in qubits Computer Networks and Communications Computer science Nonlinearity in continuous variables Reservoir computing Computer Science Applications Continuous variable Nonlinear system Artificial Intelligence Qubit Information processing Statistical physics Quantum Quantum complex networks Quantum machine learning Information Systems |
| Zdroj: | Digital.CSIC. Repositorio Institucional del CSIC instname |
| Popis: | The natural dynamics of complex networks can be harnessed for information processing purposes. A paradigmatic example are artificial neural networks used for machine learning. In this context, quantum reservoir computing (QRC) constitutes a natural extension of the use of classical recurrent neural networks using quantum resources for temporal information processing. Here, we explore the fundamental properties of QRC systems based on qubits and continuous variables. We provide analytical results that illustrate how nonlinearity enters the input–output map in these QRC implementations. We find that the input encoding through state initialization can serve to control the type of nonlinearity as well as the dependence on the history of the input sequences to be processed. We acknowledge the Spanish State Research Agency Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D (MDM-2017-0711) and Spanish State Research Agency QUARESC Project (PID2019-109094GB-C21 and -C22/AEI/10.13039/501100011033). We also acknowledge funding by CAIB through the QUAREC Project (PRD2018/47). The work of MCS has been supported by MICINN/AEI/FEDER and the University of the Balearic Islands through a 'Ramon y Cajal' Fellowship (RYC-2015-18140). GLG is funded by the Spanish Ministerio de Educación y Formación Profesional/Ministerio de Universidades and co-funded by the University of the Balearic Islands through the Beatriz Galindo Program (BG20/00085). |
| Databáze: | OpenAIRE |
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