Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Mangini, Stefano"'
Autor:
Mangini, Stefano, Cavalcanti, Daniel
We propose a method for providing unbiased estimators of multiple observables with low statistical error by utilizing informationally (over)complete measurements and tensor networks. The technique consists of an observable-specific classical optimiza
Externí odkaz:
http://arxiv.org/abs/2407.02923
Autor:
Mangini, Stefano, Cattaneo, Marco, Cavalcanti, Daniel, Filippov, Sergei, Rossi, Matteo A. C., García-Pérez, Guillermo
Publikováno v:
Phys. Rev. Research 6, 033217 (2024)
Characterization of noise in current near-term quantum devices is of paramount importance to fully use their computational power. However, direct quantum process tomography becomes unfeasible for systems composed of tens of qubits. A promising altern
Externí odkaz:
http://arxiv.org/abs/2402.08556
Quantum noise is currently limiting efficient quantum information processing and computation. In this work, we consider the tasks of reconstructing and classifying quantum states corrupted by the action of an unknown noisy channel using classical fee
Externí odkaz:
http://arxiv.org/abs/2309.11949
Autor:
Mangini, Stefano
This Ph.D. thesis provides a comprehensive review of the state-of-the-art in the field of Variational Quantum Algorithms and Quantum Machine Learning, including numerous original contributions. The first chapters are devoted to a brief summary of qua
Externí odkaz:
http://arxiv.org/abs/2306.09984
Variational quantum machine learning algorithms have become the focus of recent research on how to utilize near-term quantum devices for machine learning tasks. They are considered suitable for this as the circuits that are run can be tailored to the
Externí odkaz:
http://arxiv.org/abs/2212.09431
Autor:
Ballarin, Marco, Mangini, Stefano, Montangero, Simone, Macchiavello, Chiara, Mengoni, Riccardo
Publikováno v:
Quantum 7, 1023 (2023)
Quantum Neural Networks (QNN) are considered a candidate for achieving quantum advantage in the Noisy Intermediate Scale Quantum computer (NISQ) era. Several QNN architectures have been proposed and successfully tested on benchmark datasets for machi
Externí odkaz:
http://arxiv.org/abs/2206.02474
Publikováno v:
2022 International Joint Conference on Neural Networks (IJCNN)
Several proposals have been recently introduced to implement Quantum Machine Learning (QML) algorithms for the analysis of classical data sets employing variational learning means. There has been, however, a limited amount of work on the characteriza
Externí odkaz:
http://arxiv.org/abs/2205.10429
Autor:
Mangini, Stefano, Marruzzo, Alessia, Piantanida, Marco, Gerace, Dario, Bajoni, Daniele, Macchiavello, Chiara
Publikováno v:
Quantum Mach. Intell. 4, 13 (2022)
Quantum computing technologies are in the process of moving from academic research to real industrial applications, with the first hints of quantum advantage demonstrated in recent months. In these early practical uses of quantum computers it is rele
Externí odkaz:
http://arxiv.org/abs/2205.04127
Publikováno v:
EPJ Quantum Technology volume 9, Article number: 29 (2022)
We present a noise deconvolution technique to remove a wide class of noises when performing arbitrary measurements on qubit systems. In particular, we derive the inverse map of the most common single qubit noisy channels and exploit it at the data pr
Externí odkaz:
http://arxiv.org/abs/2112.03043
In this paper, we discuss the initial attempts at boosting understanding human language based on deep-learning models with quantum computing. We successfully train a quantum-enhanced Long Short-Term Memory network to perform the parts-of-speech taggi
Externí odkaz:
http://arxiv.org/abs/2110.06510