Results 101 to 110 of about 8,389,880 (297)

Quantum Machine Learning What Quantum Computing Means to Data Mining

, 2014
Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most ...
Wittek, Peter
core  

When Poor Exciton Dissociation Limits Photocurrents in Organic Solar Cells: Why Low Offset Non‐Fullerene Acceptor Blends Can't Be Efficient

Advanced Materials, EarlyView.
The energetic offset between the donor and the acceptor components in organic photoactive layers is central to the tradeoff between photovoltage and photocurrent losses. This Perspective covers the most important issues surrounding this topic in non‐fullerene acceptor blends, from the difficulty of accurately determining state energies and driving ...
Dieter Neher, Manasi Pranav
wiley   +1 more source

A Primer on Quantum Machine Learning

CoRR
Quantum machine learning (QML) is a computational paradigm that seeks to apply quantum-mechanical resources to solve learning problems. As such, the goal of this framework is to leverage quantum processors to tackle optimization, supervised, unsupervised and reinforcement learning, and generative modeling-among other tasks-more efficiently than ...
Su Yeon Chang, M. Cerezo
openaire   +2 more sources

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Quantum machine learning for electronic structure calculations

Nature Communications, 2018
With the rapid development of quantum computers, quantum machine learning approaches are emerging as powerful tools to perform electronic structure calculations.
Rongxin Xia, Sabre Kais
doaj   +1 more source

Large‐Scale Determination of Frontier Orbital Energies of Disordered Small‐Molecule Organic Semiconductors Using Exciplex Emission Spectra

Advanced Materials, EarlyView.
ABSTRACT Accurately knowing the frontier orbital energies of the structurally disordered small‐molecule organic semiconductors that are used in optoelectronic devices such as organic light‐emitting diodes is required to rationally improve their performance. Here, we show that these energies can be deduced with a large accuracy from the peak energies of
Christian B. McDonald, Hiroki Tomita, Giacomo Cotelli, Louka L. Brüll, Nanno G. de Rooij, Lucas R.L. Norg, Milo Clément, Reinder Coehoorn   +7 more
wiley   +1 more source

Zc(3900) observation at BESIII with QSVM method [PDF]

EPJ Web of Conferences
In recent years, quantum computing shows significant potentials in many areas. In this proceeding, we revisit the observation of the Zc(3900) resonance with quantum machine learning techniques, specifically quantum support vector machine (QSVM ...
Ding Biao, Meng Zhaoxia, Zou Jiaheng, Li Teng, Lin Tao   +4 more
doaj   +1 more source

Report and recommendations on multimedia materials for teaching and learning quantum physics [PDF]

, 2015
An international collaboration of physicists, affiliated with Multimedia Physics for Teaching and Learning (MPTL) and MERLOT, performed a survey and review of multimedia-based learning materials for quantum physics and quantum mechanics.
Mason, B, Silva, J, Santi, L, Kohnle, A, Melder, T, Girwidz, R, Dębowska, E, Arpornthip, T, Greczyło, T, Michelini, M   +9 more
core  

Quantum Machine Learning Driven Malicious User Prediction for Cloud Network Communications

, 2022
This letter proposes a novel malicious user prediction model based on quantum machine learning that estimates the vicious entity present in the communication system precedently before allocating the data in the distributed environments.
Gupta, I., Gupta, R., Makkar, A., Saxena. R., Sing, A. K.   +4 more
core   +1 more source

Phase Engineering of Nanomaterials (PEN): Evolution, Current Challenges, and Future Opportunities

Advanced Materials, EarlyView.
This review summarizes the synthesis, phase transition, advanced characterization spanning ex situ to in situ and operando techniques, and diverse applications of phase engineering of nanomaterials (PEN). It further outlines key challenges and future opportunities, such as phase stability, architecture control, and artificial intelligence (AI)‐driven ...
Ye Chen, Zhanxi Fan, Jingjie Ge, Qiyuan He, Zhuangchai Lai, Jiawei Liu, Chaoliang Tan, Hua Zhang   +7 more
wiley   +1 more source