Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Braccia, Paolo"'
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:
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
Publikováno v:
Phys.Rev.Appl. 17 (2022) 024002
Noisy-Intermediate-Scale-Quantum (NISQ) devices are nowadays starting to become available to the final user, hence potentially allowing to show the quantum speedups predicted by the quantum information theory. However, before implementing any quantum
Externí odkaz:
http://arxiv.org/abs/2107.08718
Quantum Machine Learning is where nowadays machine learning meets quantum information science. In order to implement this new paradigm for novel quantum technologies, we still need a much deeper understanding of its underlying mechanisms, before prop
Externí odkaz:
http://arxiv.org/abs/2012.05996