Results 111 to 120 of about 2,829,604 (309)

Quantum Computing Meets Deep Learning: A QCNN Model for Accurate and Efficient Image Classification

Mathematics
In deep learning, Convolutional Neural Networks (CNNs) serve as fundamental models, leveraging the correlational structure of data for tasks such as image classification and processing.
Sunil Prajapat, Manish Tomar, Pankaj Kumar, Rajesh Kumar, Athanasios V. Vasilakos   +4 more
doaj   +1 more source

Does Quantum Mechanics Breed Larger, More Intricate Quantum Theories? The Case for Experience-Centric Quantum Theory and the Interactome of Quantum Theories

We pose and address the radical question of whether quantum mechanics, known for its firm internal structure and enormous empirical success, carries in itself the genomes of larger quantum theories that have higher internal intricacy and phenomenological
Tavanfar, Alireza, Sahar Alipour, Ali T. Rezakhani, Rezakhani, Ali T., Alireza Tavanfar, Alipour, Sahar   +5 more
core   +1 more source

Single‐Chromophore Homojunction Organic Solar Cells: A Path to Simplicity and Efficiency

Advanced Materials, EarlyView.
This perspective discusses how the intrinsic optoelectronic properties of organic semiconductors, their molecular packing in the solid‐state, and internal energetic gradients within a device can enable free‐charge carrier generation in homojunction organic solar cells.
Shaun McAnally, Paul L. Burn, Paul E. Shaw   +2 more
wiley   +1 more source

Quantum computing with exciton-polariton condensates

, 2020
10.1038/s41534-020-0244-xnpj Quantum ...
Liew, T.C.H., Ghosh, Sanjib, Ghosh, S., Liew, Timothy C. H.   +3 more
core   +1 more source

Quantum cluster algebra structures on quantum Grassmannians and their quantum Schubert cells : the finite-type cases [PDF]

, 2010
We exhibit quantum cluster algebra structures on quantum Grassmannians and their quantum Schubert cells, as well as on , and . These cases are precisely those where the quantum cluster algebra is of finite type, and the structures we describe quantize ...
Launois, Stéphane, S. Launois, J. E. Grabowski, Grabowski, Jan   +3 more
core   +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

Benchmarking MedMNIST dataset on real quantum hardware

Scientific Reports
Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks.
Gurinder Singh, Hongni Jin, Kenneth M. Merz Jr.   +2 more
doaj   +1 more source

Transparent Perovskite Light‐Emitting Diodes with Conductive Oxide Top Electrodes

Advanced Materials, EarlyView.
Transparent perovskite light‐emitting diodes (TrPeLEDs) enable simultaneous display and transparency, expanding application possibilities. Using a metal oxide buffer layer and pulsed laser deposition, TrPeLEDs with diverse compositions and architectures are demonstrated.
Michele Forzatti, Sergio Martínez‐Saiz, Morena Cervino, Edoardo Stanzani, Sandra Jenatsch, Qingsen Zeng, Joo Sung Kim, Dong‐Hyeok Kim, Eojin Yoon, Tae‐Woo Lee, Daniel Tordera, Henk J. Bolink   +11 more
wiley   +1 more source

A hybrid QSVM-QNN approach for breast cancer diagnosis using quantum feature maps [PDF]

PeerJ Computer Science
Quantum assisted machine learning (QML) have the potential to outperform the classical Machine Learning models by utilizing the quantum feature maps embeddings and variational circuits.
Vikesh Yadav, Savita Kumari Sheoran, Rakesh Kumar Sheoran   +2 more
doaj   +2 more sources

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   +3 more sources