Zobrazeno 1 - 10
of 277
pro vyhledávání: '"Sanz, Mikel"'
The technological development of increasingly larger quantum processors on different quantum platforms raises the problem of how to fairly compare their performance, known as quantum benchmarking of quantum processors. This is a challenge that comput
Externí odkaz:
http://arxiv.org/abs/2407.10941
Quantum machine learning, as an extension of classical machine learning that harnesses quantum mechanics, facilitates effiient learning from data encoded in quantum states. Training a quantum neural network typically demands a substantial labeled tra
Externí odkaz:
http://arxiv.org/abs/2405.18230
Autor:
Ramirez, Ibai, Pino, Joel, Pardo, David, Sanz, Mikel, del Rio, Luis, Ortiz, Alvaro, Morozovska, Kateryna, Aizpurua, Jose I.
Transformers are vital assets for the reliable and efficient operation of power and energy systems. They support the integration of renewables to the grid through improved grid stability and operation efficiency. Monitoring the health of transformers
Externí odkaz:
http://arxiv.org/abs/2405.06443
Autor:
Lazar, Jeffrey, Olavarrieta, Santiago Giner, Gatti, Giancarlo, Argüelles, Carlos A., Sanz, Mikel
Ever-increasing amount of data is produced by particle detectors in their quest to unveil the laws of Nature. The large data rate requires the use of specialized triggers that promptly reduce the data rate to a manageable level; however, in doing so,
Externí odkaz:
http://arxiv.org/abs/2402.19306
Cram\'er-Rao constitutes a crucial lower bound for the mean squared error of an estimator in frequentist parameter estimation, albeit paradoxically demanding highly accurate prior knowledge of the parameter to be estimated. Indeed, this information i
Externí odkaz:
http://arxiv.org/abs/2402.15242
Kernel methods play a crucial role in machine learning and the Embedding Quantum Kernels (EQKs), an extension to quantum systems, have shown very promising performance. However, choosing the right embedding for EQKs is challenging. We address this by
Externí odkaz:
http://arxiv.org/abs/2401.04642
We propose a quantum lidar protocol to jointly estimate the range and velocity of a target by illuminating it with a single beam of pulsed displaced squeezed light. In the lossless scenario, we show that the mean-squared errors of both range and velo
Externí odkaz:
http://arxiv.org/abs/2311.14546
Autor:
Rodriguez-Grasa, Pablo, Ibarrondo, Ruben, Gonzalez-Conde, Javier, Ban, Yue, Rebentrost, Patrick, Sanz, Mikel
Classical information loading is an essential task for many processing quantum algorithms, constituting a cornerstone in the field of quantum machine learning. In particular, the embedding techniques based on Hamiltonian simulation techniques enable
Externí odkaz:
http://arxiv.org/abs/2311.11751
We propose a hybrid quantum-classical approximate optimization algorithm for photonic quantum computing, specifically tailored for addressing continuous-variable optimization problems. Inspired by counterdiabatic protocols, our algorithm significantl
Externí odkaz:
http://arxiv.org/abs/2307.14853
Publikováno v:
Quantum 8, 1297 (2024)
Loading functions into quantum computers represents an essential step in several quantum algorithms, such as quantum partial differential equation solvers. Therefore, the inefficiency of this process leads to a major bottleneck for the application of
Externí odkaz:
http://arxiv.org/abs/2307.10917