Results 91 to 100 of about 8,389,880 (297)

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

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 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

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

A Future with Quantum Machine Learning

Computer, 2018
Could combining quantum computing and machine learning with Moore’s law produce a true “rebooted computer”? This article posits that a three-technology hybrid-computing approach might yield sufficiently improved answers to a broad class of problems such that energy efficiency will no longer be the dominant concern.
openaire   +2 more sources

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

Deep learning for quantum sciences: Selected topics

This chapter discusses more specialized examples on how machine learning can be used to solve problems in quantum sciences. We start by explaining the concept of differentiable programming and its use cases in quantum sciences.
Carleo, Giuseppe, Stornati, Paolo, Tomza, Michał, Carrasquilla, Juan, Krems, Roman, Arnold, Julian, Płodzień, Marcin, Dauphin, Alexandre, Okuła, Robert, Koch, Rouven, van Nieuwenburg, Evert, Büttner, Miriam, Gabrié, Marylou, Dunjko, Vedran, Requena, Borja, Dawid, Anna, Muñoz-Gil, Gorka, Gresch, Alexander, Vicentini, Filippo, Marquardt, Florian, Lewenstein, Maciej, Greplová, Eliška, Donatella, Kaelan, Cervera-Lierta, Alba, Wang, Lei, Nicoli, Kim A., Wetzel, Sebastian J., Vargas-Hernández, Rodrigo A.   +27 more
core   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 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

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