Thesis etd-02062026-090521 |
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Thesis type
Tesi di laurea magistrale
URN
etd-02062026-090521
Thesis title
Evaluating Quantum Reservoir Computing: Richness, Memory and Prediction
Department
INFORMATICA
Course of study
INFORMATICA
Supervisors
.
relatore Prof. Bacciu, Davide
relatore Prof. Gallicchio, Claudio
relatore Prof. Ceni, Andrea
relatore Prof. Gallicchio, Claudio
relatore Prof. Ceni, Andrea
Keywords
- echo state network
- neural network
- quantum machine learning
- quantum neural network
- quantum reservoir network
- recurrent network
- reservoir computing
Graduation session start date
27/02/2026
Availability
Full
Abstract (Inglese)
Abstract (Italiano)
The aim of this thesis is to evaluate a reservoir computing model inspired by the dynamics of a quantum system proposed in the literature.
The work involves comparing this model with a traditional reservoir computing approach, with the aim of identifying the parametric conditions that make the use of a quantum reservoir advantageous in solving classic time series processing problems.
The evaluation includes analyzing the memory capacity, estimating the effective dimensionality using techniques based on Principal Component Analysis, and measuring the predictive capacity on autoregressive and chaotic time series.
The work involves comparing this model with a traditional reservoir computing approach, with the aim of identifying the parametric conditions that make the use of a quantum reservoir advantageous in solving classic time series processing problems.
The evaluation includes analyzing the memory capacity, estimating the effective dimensionality using techniques based on Principal Component Analysis, and measuring the predictive capacity on autoregressive and chaotic time series.
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| GNicassi...is_v2.pdf | 5.36 Mb |
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