Zobrazeno 1 - 10
of 424
pro vyhledávání: '"SAUVAGE, Frédéric"'
Autor:
Sauvage, Frederic, Larocca, Martin
Classical shadows (CS) have emerged as a powerful way to estimate many properties of quantum states based on random measurements and classical post-processing. In their original formulation, they come with optimal (or close to) sampling complexity gu
Externí odkaz:
http://arxiv.org/abs/2408.05279
Autor:
Ragone, Michael, Bakalov, Bojko N., Sauvage, Frédéric, Kemper, Alexander F., Marrero, Carlos Ortiz, Larocca, Martin, Cerezo, M.
Publikováno v:
Nature Communications 15, 7172 (2024)
Variational quantum computing schemes train a loss function by sending an initial state through a parametrized quantum circuit, and measuring the expectation value of some operator. Despite their promise, the trainability of these algorithms is hinde
Externí odkaz:
http://arxiv.org/abs/2309.09342
Classical simulation of quantum dynamics plays an important role in our understanding of quantum complexity, and in the development of quantum technologies. Compared to other techniques for efficient classical simulations, methods relying on the Lie-
Externí odkaz:
http://arxiv.org/abs/2308.01432
Autor:
Côté, Jeremy, Sauvage, Frédéric, Larocca, Martín, Jonsson, Matías, Cincio, Lukasz, Albash, Tameem
Publikováno v:
Quantum Sci. Technol. 8 045033 (2023)
Quantum annealing is a continuous-time heuristic quantum algorithm for solving or approximately solving classical optimization problems. The algorithm uses a schedule to interpolate between a driver Hamiltonian with an easy-to-prepare ground state an
Externí odkaz:
http://arxiv.org/abs/2212.02624
Publikováno v:
npj Quantum Inf 10, 12 (2024)
Despite the great promise of quantum machine learning models, there are several challenges one must overcome before unlocking their full potential. For instance, models based on quantum neural networks (QNNs) can suffer from excessive local minima an
Externí odkaz:
http://arxiv.org/abs/2210.09974
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:
Quantum Sci. Technol. 9 015029 (2024)
Practical success of quantum learning models hinges on having a suitable structure for the parameterized quantum circuit. Such structure is defined both by the types of gates employed and by the correlations of their parameters. While much research h
Externí odkaz:
http://arxiv.org/abs/2207.14413
Autor:
Alderete, C. Huerta, Gordon, Max Hunter, Sauvage, Frederic, Sone, Akira, Sornborger, Andrew T., Coles, Patrick J., Cerezo, M.
Publikováno v:
Phys. Rev. Lett. 129, 190501 (2022)
In a standard Quantum Sensing (QS) task one aims at estimating an unknown parameter $\theta$, encoded into an $n$-qubit probe state, via measurements of the system. The success of this task hinges on the ability to correlate changes in the parameter
Externí odkaz:
http://arxiv.org/abs/2206.09919
Autor:
Larocca, Martin, Sauvage, Frederic, Sbahi, Faris M., Verdon, Guillaume, Coles, Patrick J., Cerezo, M.
Publikováno v:
PRX Quantum 3, 030341 (2022)
Quantum Machine Learning (QML) models are aimed at learning from data encoded in quantum states. Recently, it has been shown that models with little to no inductive biases (i.e., with no assumptions about the problem embedded in the model) are likely
Externí odkaz:
http://arxiv.org/abs/2205.02261