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pro vyhledávání: '"YANO, Hiroshi"'
Deep learning has seen substantial achievements, with numerical and theoretical evidence suggesting that singularities of statistical models are considered a contributing factor to its performance. From this remarkable success of classical statistica
Externí odkaz:
http://arxiv.org/abs/2411.16396
Publikováno v:
Phys. Rev. Research 6, 013205 (2024)
The variational quantum eigensolver (VQE) stands as a prominent quantum-classical hybrid algorithm for near-term quantum computers to obtain the ground states of molecular Hamiltonians in quantum chemistry. However, due to the non-commutativity of th
Externí odkaz:
http://arxiv.org/abs/2307.00766
Autor:
Yano, Hiroshi, Yamamoto, Naoki
Publikováno v:
J. Phys. A: Math. Theor. 56, 405301 (2023)
Quantum state estimation (or state tomography) is an indispensable task in quantum information processing. Because full state tomography that determines all elements of the density matrix is computationally demanding, one usually takes the strategy o
Externí odkaz:
http://arxiv.org/abs/2304.10949
Size-Dependence of the Electrochemical Activity of Platinum Particles in the 1 to 2 Nanometer Range.
Autor:
Yano, Hiroshi1 (AUTHOR) hiroshi.yano@toyota-boshoku.com, Iwasaki, Kouta1 (AUTHOR)
Publikováno v:
Surfaces (2571-9637). Sep2024, Vol. 7 Issue 3, p472-481. 10p.
Variational quantum algorithms (VQAs) are promising methods that leverage noisy quantum computers and classical computing techniques for practical applications. In VQAs, the classical optimizers such as gradient-based optimizers are utilized to adjus
Externí odkaz:
http://arxiv.org/abs/2106.10981
Quantum classifiers provide sophisticated embeddings of input data in Hilbert space promising quantum advantage. The advantage stems from quantum feature maps encoding the inputs into quantum states with variational quantum circuits. A recent work sh
Externí odkaz:
http://arxiv.org/abs/2106.09415
Autor:
Tamari, Masato, Del Bel, Kate L., Ver Heul, Aaron M., Zamidar, Lydia, Orimo, Keisuke, Hoshi, Masato, Trier, Anna M., Yano, Hiroshi, Yang, Ting-Lin, Biggs, Catherine M., Motomura, Kenichiro, Shibuya, Rintaro, Yu, Chuyue D., Xie, Zili, Iriki, Hisato, Wang, Zhen, Auyeung, Kelsey, Damle, Gargi, Demircioglu, Deniz, Gregory, Jill K., Hasson, Dan, Dai, Jinye, Chang, Rui B., Morita, Hideaki, Matsumoto, Kenji, Jain, Sanjay, Van Dyken, Steven, Milner, Joshua D., Bogunovic, Dusan, Hu, Hongzhen, Artis, David, Turvey, Stuart E., Kim, Brian S.
Publikováno v:
In Cell 4 January 2024 187(1):44-61
Publikováno v:
IEEE Transactions on Quantum Engineering, vol. 2, pp. 1-14, 2021, Art no. 3103214
Recent days have witnessed significant interests in applying quantum-enhanced techniques for solving a variety of machine learning tasks. Variational methods that use quantum resources of imperfect quantum devices with the help of classical computing
Externí odkaz:
http://arxiv.org/abs/2005.14382
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