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pro vyhledávání: '"Friedrich, Lucas"'
Quantum computing with qudits, an extension of qubits to multiple levels, is a research field less mature than qubit-based quantum computing. However, qudits can offer some advantages over qubits, by representing information with fewer separated comp
Externí odkaz:
http://arxiv.org/abs/2409.17716
Variational Quantum Algorithms (VQAs) have emerged as pivotal strategies for attaining quantum advantages in diverse scientific and technological domains, notably within Quantum Neural Networks. However, despite their potential, VQAs encounter signif
Externí odkaz:
http://arxiv.org/abs/2405.08190
Autor:
Friedrich, Lucas, Maziero, Jonas
The rapid development of quantum computers promises transformative impacts across diverse fields of science and technology. Quantum neural networks (QNNs), as a forefront application, hold substantial potential. Despite the multitude of proposed mode
Externí odkaz:
http://arxiv.org/abs/2402.06026
The preparation of quantum states serves as a pivotal subroutine across various domains, including quantum communication protocols, quantum computing, and the exploration of quantum correlations and other resources within physical systems. Building u
Externí odkaz:
http://arxiv.org/abs/2402.04212
Autor:
Friedrich, Lucas, Maziero, Jonas
Variational Quantum Algorithms (VQAs) employ quantum circuits parameterized by $U$, optimized using classical methods to minimize a cost function. While VQAs have found broad applications, certain challenges persist. Notably, a significant computatio
Externí odkaz:
http://arxiv.org/abs/2310.17402
Autor:
Friedrich, Lucas, Maziero, Jonas
Publikováno v:
Scientific Reports 13, 9978 (2023)
Although we are currently in the era of noisy intermediate scale quantum devices, several studies are being conducted with the aim of bringing machine learning to the quantum domain. Currently, quantum variational circuits are one of the main strateg
Externí odkaz:
http://arxiv.org/abs/2301.06883
Autor:
Friedrich, Lucas, Maziero, Jonas
Publikováno v:
Quantum Inf. Process. 23, 131 (2024)
In the era of noisy intermediate-scale quantum devices, variational quantum algorithms (VQAs) stand as a prominent strategy for constructing quantum machine learning models. These models comprise both a quantum and a classical component. The quantum
Externí odkaz:
http://arxiv.org/abs/2212.14426
Autor:
Friedrich, Lucas, Maziero, Jonas
Publikováno v:
Phys. Rev. A 106, 042433 (2022)
Variational quantum algorithms (VQAs) are among the most promising algorithms in the era of Noisy Intermediate Scale Quantum Devices. Such algorithms are constructed using a parameterization U($\pmb{\theta}$) with a classical optimizer that updates t
Externí odkaz:
http://arxiv.org/abs/2205.13418
Autor:
Friedrich, Lucas, Maziero, Jonas
Publikováno v:
Quantum Inf. Process. 22, 132 (2023)
With the rapid development of quantum computers, several applications are being proposed for them. Quantum simulations, simulation of chemical reactions, solution of optimization problems and quantum neural networks (QNNs) are some examples. However,
Externí odkaz:
http://arxiv.org/abs/2205.08059
Autor:
Arndt, Andreas
Publikováno v:
Hegel-Studien, 2001 Jan 01. 36, 248-251.
Externí odkaz:
https://www.jstor.org/stable/26589473