Zobrazeno 1 - 10
of 193
pro vyhledávání: '"quantum-inspired machine learning"'
Autor:
Borella, Lorenzo, Coppi, Alberto, Pazzini, Jacopo, Stanco, Andrea, Trenti, Marco, Triossi, Andrea, Zanetti, Marco
Tensor Networks (TNs) are a computational paradigm used for representing quantum many-body systems. Recent works have shown how TNs can also be applied to perform Machine Learning (ML) tasks, yielding comparable results to standard supervised learnin
Externí odkaz:
http://arxiv.org/abs/2409.16075
Autor:
Shu, Runqiu, Liu, Bowen, Xiong, Zhaoping, Cui, Xiaopeng, Li, Yunting, Cui, Wei, Yung, Man-Hong, Qiao, Nan
Molecular docking is an important tool for structure-based drug design, accelerating the efficiency of drug development. Complex and dynamic binding processes between proteins and small molecules require searching and sampling over a wide spatial ran
Externí odkaz:
http://arxiv.org/abs/2401.12999
Autor:
Ran, Shi-Ju, Su, Gang
Publikováno v:
Intelligent Computing 2, 0061 (2023)
It is a critical challenge to simultaneously gain high interpretability and efficiency with the current schemes of deep machine learning (ML). Tensor network (TN), which is a well-established mathematical tool originating from quantum mechanics, has
Externí odkaz:
http://arxiv.org/abs/2311.11258
Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within classical computational frameworks. However, current review literature of
Externí odkaz:
http://arxiv.org/abs/2308.11269
Autor:
Vijay Anand R, Magesh G, Alagiri I, Madala Guru Brahmam, Balamurugan Balusamy, Francesco Benedetto
Publikováno v:
IEEE Access, Vol 12, Pp 80397-80417 (2024)
There is an ever-growing need for optimizing efficient nano and quantum communication systems and this demand calls for more advanced optimization techniques than those that have been around for quite some time. The conventional genetic algorithms (G
Externí odkaz:
https://doaj.org/article/39a89968a496496884fba47fcc0286b8
Autor:
Felser, Timo, Trenti, Marco, Sestini, Lorenzo, Gianelle, Alessio, Zuliani, Davide, Lucchesi, Donatella, Montangero, Simone
Tensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems. Exploiting a tree tensor network, we apply a quantum-inspired machine learning technique to
Externí odkaz:
http://arxiv.org/abs/2004.13747
Akademický článek
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Autor:
Zhao, Zhikuan, Fitzsimons, Jack K., Rebentrost, Patrick, Dunjko, Vedran, Fitzsimons, Joseph F.
Machine learning has recently emerged as a fruitful area for finding potential quantum computational advantage. Many of the quantum enhanced machine learning algorithms critically hinge upon the ability to efficiently produce states proportional to h
Externí odkaz:
http://arxiv.org/abs/1804.00281
Autor:
Trung Q. Duong, James Adu Ansere, Bhaskara Narottama, Vishal Sharma, Octavia A. Dobre, Hyundong Shin
Publikováno v:
IEEE Open Journal of Vehicular Technology, Vol 3, Pp 375-387 (2022)
Quantum computing is envisaged as an evolving paradigm for solving computationally complex optimization problems with a large-number factorization and exhaustive search. Recently, there has been a proliferating growth of the size of multi-dimensional
Externí odkaz:
https://doaj.org/article/55207f74cce84ea18b6daa215ca93d38
Autor:
Shi-Ju Ran, Gang Su
Publikováno v:
Intelligent Computing, Vol 2 (2023)
It is a critical challenge to simultaneously achieve high interpretability and high efficiency with the current schemes of deep machine learning (ML). The tensor network (TN), a well-established mathematical tool originating from quantum mechanics, h
Externí odkaz:
https://doaj.org/article/357120c9ae714fdcae987b31ab154d5c