6 papers
Entanglement and Classical Simulability in Quantum Extreme Learning Machines
A. De Lorenzis, M. P. Casado, N. Lo Gullo +3
Quantum Machine Learning (QML) has emerged as a promising framework for exploring how quantum dynamics may enhance data processing tasks. Here we investigate Quantum Extreme Learni…
Flat-band energy filtering in interacting systems: conditions for improving thermoelectric performances
F. Cosco, R. Tuovinen, F. Plastina +1
Motivated by recent theoretical and experimental studies on the role of flatbands in the thermoelectric properties of NiInSn compounds, we investigate electron tran…
Bayesian mitigation of measurement errors in multiqubit experiments
F. Cosco, F. Plastina, N. Lo Gullo
In Phys. Rev. A 108, L060402 (2023), we introduced a Bayesian measurement error mitigation algorithm, which leveraged complete information from the readout signal, and validated th…
Forecasting Low-Dimensional Turbulence via Multi-Dimensional Hybrid Quantum Reservoir Computing
L. Salatino, L. Mariani, A. Giordano +9
The prediction of complex dynamics remains an open problem across many domains of physics, where nonlinearities and multiscale interactions severely limit the reliability of conven…
Harnessing Quantum Extreme Learning Machines for image classification
A. De Lorenzis, M. P. Casado, M. P. Estarellas +5
Interest in quantum machine learning is increasingly growing due to its potential to offer more efficient solutions for problems that are difficult to tackle with classical methods…
Memory-Augmented Hybrid Quantum Reservoir Computing
J. Settino, L. Salatino, L. Mariani +9
Reservoir computing (RC) is an effective method for predicting chaotic systems by using a high-dimensional dynamic reservoir with fixed internal weights, while keeping the learning…