Results 131 to 140 of about 23,653 (255)

Quantum machine learning with differential privacy. [PDF]

Sci Rep, 2023
Watkins WM, Chen SY, Yoo S.
europepmc   +1 more source

Femtosecond‐Laser‐Induced Physical Unclonable Random Maze Structure for Storage‐Free Encryption

Advanced Science, EarlyView.
Femtosecond‐laser‐induced gold random maze structures serve as multimodal physical unclonable functions for storage‐free encryption. Their stochastic optical, electrical, and Raman responses are generated by plasmon‐assisted Marangoni formation and converted into AES‐compatible keys without permanent secret‐key storage, offering a portable route toward
Shiru Jiang, Hongliang Li, Shengjie Ma, Sang‐Shin Lee, Lei Wang, Mengyun Hu, Heping Zeng   +6 more
wiley   +1 more source

Oncological Applications of Quantum Machine Learning. [PDF]

Technol Cancer Res Treat, 2023
Rahimi M, Asadi F.
europepmc   +1 more source

Electromagnetic Radiation Stimulated Learning in Perovskite Nickelates

Advanced Science, EarlyView.
ABSTRACT Biological plasticity refers to the ability of synapses to strengthen or weaken over time. These adaptive properties play a fundamental role in learning and memory, spanning many orders of magnitude in timescales. Short‐term plasticity (STP) arises from rapid correlative activity, while long‐term plasticity (LTP) is governed by slower ...
Ranjan Kumar Patel, Kabir Zama, Matthew Smart, Ramya Eathirajan, Ivan Seskar, Narayan Mandayam, Guangxin Ni, Martin Mönnigmann, Shriram Ramanathan   +8 more
wiley   +1 more source

Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets. [PDF]

Front Artif Intell, 2023
Peixoto MC, Castro NF, Crispim Romão M, Oliveira MGJ, Ochoa I.   +4 more
europepmc   +1 more source

Quantum Machine Learning for Malware Classification

In a context of malicious software detection, machine learning (ML) is widely used to generalize to new malware. However, it has been demonstrated that ML models can be fooled or may have generalization problems on malware that has never been seen. We investigate the possible benefits of quantum algorithms for classification tasks.
Grégoire Barrué, Tony Quertier
openaire   +2 more sources

Multiferroic‐Centric Materials and Systems Engineering for Battery Applications: An Insight Into Mechanisms, Strategies, and Characterizations

Advanced Science, EarlyView.
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su, Yanda Zhu, Hao Peng, Manman Li, Xuzihan Zhang, Meixiao Wu, Siyuan Zhang, Yuhan Zeng, Jiwen Liao, Ming Luo, Hetaishan Huang, Yutong Wang, Sean Li, Wenxian Li   +13 more
wiley   +1 more source

Quantum Machine Learning: Quantum Kernel Methods

CoRR
Quantum algorithms based on quantum kernel methods have been investigated previously [1]. A quantum advantage is derived from the fact that it is possible to construct a family of datasets for which, only quantum processing can recognise the intrinsic labelling patterns, while for classical computers the dataset looks like noise.
openaire   +2 more sources

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

Advanced Science, EarlyView.
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi, Minying Low, Peng Xiu, Kresten Lindorff‐Larsen, Yong Wang   +4 more
wiley   +1 more source

Quantum machine learning beyond kernel methods. [PDF]

Nat Commun, 2023
Jerbi S, Fiderer LJ, Poulsen Nautrup H, Kübler JM, Briegel HJ, Dunjko V.   +5 more
europepmc   +1 more source