Results 71 to 80 of about 21,815 (240)

Optoelectronic Synaptic Devices Using Molecular Telluride Phase‐Change Inks for Three‐Factor Learning

Advanced Functional Materials, EarlyView.
Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner, Dhananjeya Kumaar, Giacomo Torti, Killian Keller, Till Zellweger, Florian M. Schenk, Christoph Weilenmann, Daniel Chelladurai, Vara Prasad Jonnalagadda, Ghazi Sarwat Syed, Vanessa Wood, Jürg Leuthold, Alexandros Emboras, Maksym Yarema, Mathieu Luisier   +14 more
wiley   +1 more source

Atomic Layer Deposition in Transistors and Monolithic 3D Integration

Advanced Functional Materials, EarlyView.
Transistors are fundamental building blocks of modern electronics. This review summarizes recent progress in atomic layer deposition (ALD) for the synthesis of two‐dimensional (2D) metal oxides and transition‐metal dichalcogenides (TMDCs), with particular emphasis on their enabling role in monolithic three‐dimensional (M3D) integration for next ...
Yue Liu, Taoyu Zou, Cheng‐Yan Xu, Liang Zhen, Yang Li, Yong‐Young Noh   +5 more
wiley   +1 more source

Taming the Immiscibility of Gold, Iron, and Boron to Craft Chemodegradable Nanoparticles for Multimodal Imaging and Radiotherapy

Advanced Healthcare Materials, EarlyView.
Over half of cancer patients undergo radiotherapy. Laser ablation enabled the synthesis of immiscible Au‐Fe‐B nanoparticles designed as degradable bimodal radiosensitizers for X‐ray radiotherapy (XRT), boron neutron capture therapy (BNCT), and bimodal imaging for X‐ray computed tomography (CT) and magnetic resonance imaging (MRI). These nanosensitizers
Michael Bissoli, Clara M.G. de Faria, Veronica Torresan, Maria Assunta Lacavalla, Mattia Cattelan, Denis Badocco, Paolo Pastore, Pasquina Marzola, Laura Cansolino, Cinzia Ferrari, Ian Postuma, Riccardo Vago, Silva Bortolussi, Antonello E. Spinelli, Daniel Forrer, Vincenzo Amendola   +15 more
wiley   +1 more source

Quantum learning machines

, 2023
Physical learning machines, be they classical or quantum, are necessarily dissipative systems. The rate of energy dissipation decreases as the learning error rate decreases linking thermodynamic efficiency and learning efficiency. In the classical case the energy is dissipated as heat.
openaire   +2 more sources

Quantum Chemistry Meets Machine Learning

CHIMIA, 2019
In this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the power brought by kernel-based approaches in the large-scale screening of homogeneous ...
Fabrizio, Alberto, Meyer, Benjamin, Fabregat, Raimon, Corminboeuf, Clemence   +3 more
openaire   +4 more sources

Opinion: Gavage Administration of MXene as a Route‐Specific Alternative to Intravenous Injection into the Bloodstream of Laboratory Animals for Reducing Systemic Nanotoxicity Risks in Immunosuppression and Post‐Transplantation Models with Bile Acid Modification

Advanced Healthcare Materials, EarlyView.
Recent studies reported immunosuppressive properties of specific MXene nanomaterials. Their intravenous injection into the bloodstream of laboratory animals has been a common delivery method to suppress systemic inflammation and prevent transplant rejection.
Alireza Rafieerad, M. Akif Rahman, Ahmad Amiri   +2 more
wiley   +1 more source

Performance of Quantum Annealing Machine Learning Classification Models on ADMET Datasets

IEEE Access
The Quantum Annealer built by D-Wave, known as Advantage, is currently the largest quantum computer in the world, featuring a topology called “Pegasus.” This groundbreaking system opens new possibilities for solving highly complex problems.
Hadi Salloum, Kamil Sabbagh, Vladislav Savchuk, Ruslan Lukin, Osama Orabi, Marat Isangulov, Manuel Mazzara   +6 more
doaj   +1 more source

Potential and limitations of random Fourier features for dequantizing quantum machine learning [PDF]

Quantum
Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning where {parameterized quantum circuits} (PQCs) are used as learning models.
Ryan Sweke, Erik Recio-Armengol, Sofiene Jerbi, Elies Gil-Fuster, Bryce Fuller, Jens Eisert, Johannes Jakob Meyer   +6 more
doaj   +1 more source

Ferroelectric Quantum Dots for Retinomorphic In‐Sensor Computing

Advanced Materials, EarlyView.
This work has provided a protocol for fabricating retinomorphic phototransistors by integrating ferroelectric ligands with quantum dots. The resulting device combines ferroelectricity, optical responsiveness, and low‐power operation to enable adaptive signal amplification and high recognition accuracy under low‐light conditions, while supporting ...
Tingyu Long, Huanyu Zhou, Jaewan Ko, Hongwei Tan, Jaemin Lim, Yanfei Zhao, Daehan Kang, Eojin Yoon, Gyeong‐Tak Go, Somin Kim, Seung‐Woo Lee, Chan‐Yul Park, Hyojun Choi, Hyeran Kim, Hyung Joong Yun, Sung Hyuk Park, Kwan Sik Park, Jeong Woo Park, Mungeun Kim, Yong Soo Cho, Ho Won Jang, Wenqiang Yang, Min Hyuk Park, Wan Ki Bae, Sebastiaan van Dijken, Joona Bang, Tae‐Woo Lee   +26 more
wiley   +1 more source

Representation Learning via Quantum Neural Tangent Kernels

PRX Quantum, 2022
Variational quantum circuits are used in quantum machine learning and variational quantum simulation tasks. Designing good variational circuits or predicting how well they perform for given learning or optimization tasks is still unclear. Here we discuss
Junyu Liu, Francesco Tacchino, Jennifer R. Glick, Liang Jiang, Antonio Mezzacapo   +4 more
doaj   +1 more source