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Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising model for Quantum Machine Learning (QML). In this work we tie their heuristic success to two facts. First, that when randomly initialized, they can only operate on the i
Externí odkaz:
http://arxiv.org/abs/2408.12739
The spectral gap of local random quantum circuits is a fundamental property that determines how close the moments of the circuit's unitaries match those of a Haar random distribution. When studying spectral gaps, it is common to bound these quantitie
Externí odkaz:
http://arxiv.org/abs/2408.11201
Parametrized and random unitary (or orthogonal) $n$-qubit circuits play a central role in quantum information. As such, one could naturally assume that circuits implementing symplectic transformation would attract similar attention. However, this is
Externí odkaz:
http://arxiv.org/abs/2405.10264
Publikováno v:
Quantum Mach. Intell. 6, 54 (2024)
A basic primitive in quantum information is the computation of the moments $\mathbb{E}_U[{\rm Tr}[U\rho U^\dagger O]^t]$. These describe the distribution of expectation values obtained by sending a state $\rho$ through a random unitary $U$, sampled f
Externí odkaz:
http://arxiv.org/abs/2403.01706
Autor:
Cerezo, M., Larocca, Martin, García-Martín, Diego, Diaz, N. L., Braccia, Paolo, Fontana, Enrico, Rudolph, Manuel S., Bermejo, Pablo, Ijaz, Aroosa, Thanasilp, Supanut, Anschuetz, Eric R., Holmes, Zoë
A large amount of effort has recently been put into understanding the barren plateau phenomenon. In this perspective article, we face the increasingly loud elephant in the room and ask a question that has been hinted at by many but not explicitly add
Externí odkaz:
http://arxiv.org/abs/2312.09121
In the past few decades, researchers have created a veritable zoo of quantum algorithm by drawing inspiration from classical computing, information theory, and even from physical phenomena. Here we present quantum algorithms for parallel-in-time simu
Externí odkaz:
http://arxiv.org/abs/2308.12944
Autor:
Andrea Degl’Innocenti, Clarissa Braccia, Giada Graziana Genchi, Nicoletta di Leo, Luca Leoncino, Federico Catalano, Andrea Armirotti, Gianni Ciofani
Publikováno v:
ACS Omega, Vol 9, Iss 27, Pp 29226-29233 (2024)
Externí odkaz:
https://doaj.org/article/bd2adbdf4d7d4c18b9813215feaa0a69
Autor:
Lucia Falcigno, Simone Braccia, Rosa Bellavita, Gabriella D’Auria, Annarita Falanga, Stefania Galdiero
Publikováno v:
Frontiers in Drug Discovery, Vol 4 (2024)
Antimicrobial resistance has significantly increased over the last 30 years, prompting scientists to continuously look for novel, effective ways to combat drug-resistant bacteria and fungi. Due to their broad range of effectiveness, ease of synthesis
Externí odkaz:
https://doaj.org/article/b3e5f1ac88fd4e3e87937f0c38d98bad
Autor:
Nguyen, Quynh T., Schatzki, Louis, Braccia, Paolo, Ragone, Michael, Coles, Patrick J., Sauvage, Frederic, Larocca, Martin, Cerezo, M.
Publikováno v:
PRX Quantum 5, 020328 (2024)
Quantum neural network architectures that have little-to-no inductive biases are known to face trainability and generalization issues. Inspired by a similar problem, recent breakthroughs in machine learning address this challenge by creating models e
Externí odkaz:
http://arxiv.org/abs/2210.08566
Autor:
Ragone, Michael, Braccia, Paolo, Nguyen, Quynh T., Schatzki, Louis, Coles, Patrick J., Sauvage, Frederic, Larocca, Martin, Cerezo, M.
Recent advances in classical machine learning have shown that creating models with inductive biases encoding the symmetries of a problem can greatly improve performance. Importation of these ideas, combined with an existing rich body of work at the n
Externí odkaz:
http://arxiv.org/abs/2210.07980