Results 71 to 80 of about 23,653 (255)

On a quantum inspired approach to train machine learning models

Applied AI Letters, 2023
In this work, a novel technique to train machine learning models is introduced, which is based on digital simulations of certain types of quantum systems.
Jean Michel Sellier
doaj   +1 more source

3D Anodic Alumina Nanoarchitectures: A Decade of Progress from Foundational Science to Functional Metamaterials

Advanced Materials, EarlyView.
Ordered three‐dimensional anodic aluminum oxide (3D‐AAO) nanoarchitectures with longitudinal and transverse pores enable architecture‐driven metamaterials. The review maps fabrication advances, including hybrid pulse anodization, and shows how 3D‐AAO templates tailor properties across magnetism, energy, catalysis, and sensing.
Marisol Martín‐González
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

A quantum-inspired classifier for clonogenic assay evaluations

Scientific Reports, 2021
Recent advances in Quantum Machine Learning (QML) have provided benefits to several computational processes, drastically reducing the time complexity.
Giuseppe Sergioli, Carmelo Militello, Leonardo Rundo, Luigi Minafra, Filippo Torrisi, Giorgio Russo, Keng Loon Chow, Roberto Giuntini   +7 more
doaj   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +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

Quantum Machine Learning Playground

IEEE Computer Graphics and Applications
Accepted to IEEE Computer Graphics and Applications.
Pascal Debus, Sebastian Issel, Kilian Tscharke   +2 more
openaire   +3 more sources

New‐Era Polymer Thermoelectrics: Material Innovations, Doping Frontiers, Decoupling Strategies, and Unconventional Applications

Advanced Materials, EarlyView.
The field of polymer thermoelectrics is entering a new era, featuring breakthroughs in addressing the conventional performance disparity between p‐type and n‐type polymers, pioneering doping frontiers, and sophisticated decoupling strategies. This review explores innovations in molecular design and superior stabilities, bridging the gap from ...
Suhao Wang
wiley   +1 more source

Turning Unpredictable Biomolecule Adsorption to Controlled Corona Formation: Focus on Carbon Nanomaterials

Advanced Materials, EarlyView.
Controlling the protein corona formation onto carbon nanomaterials (CNMs) enhances their functionalities as platforms for cancer theranostics. Here, we reviewed the effects of the intrinsic and acquired properties of CNMs on protein corona formation, the consequent biological and toxicological outcomes, and the strategies to reshape corona formation ...
Yajuan Zou, Yalei Hu, Jie Yu, Naoki Komatsu, Yuta Nishina, Alberto Bianco   +5 more
wiley   +1 more source

Quantum tangent kernel

Physical Review Research
The quantum kernel method is one of the key approaches to quantum machine learning, which has the advantage of not requiring optimization and its theoretical simplicity.
Norihito Shirai, Kenji Kubo, Kosuke Mitarai, Keisuke Fujii   +3 more
doaj   +1 more source