Results 91 to 100 of about 33,969 (261)

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

Scalable parameterized quantum circuits classifier

Scientific Reports
As a generalized quantum machine learning model, parameterized quantum circuits (PQC) have been found to perform poorly in terms of classification accuracy and model scalability for multi-category classification tasks. To address this issue, we propose a
Xiaodong Ding, Zhihui Song, Jinchen Xu, Yifan Hou, Tian Yang, Zheng Shan   +5 more
doaj   +1 more source

An experience-based classification of quantum bugs in quantum software

Computing
Abstract As quantum computers continue to improve in quality and scale, there is a growing need for accessible software frameworks for programming them. However, the unique behavior of quantum systems means specialized approaches, beyond traditional software development, are required.
Nils Quetschlich, Olivia Di Matteo
openaire   +2 more sources

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

Classification of quantum correlations via quantum-inspired machine learning

Quantum Machine Intelligence
Abstract Quantum information theory, and in particular, the theory of quantum state discrimination, has enabled the development of a supervised multi-class classification algorithm. Inspired by the Pretty Good Measurement (PGM), a quantum-inspired classifier named the PGM ...
Giuseppe Sergioli, Andrés Camilo Granda Arango, Carlo Cuccu, Stefania Centrone, Carla Rieger, Roberto Giuntini   +5 more
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

On the classification of quantum symmetries

20 pages; minor ...
Gordienko, A. S., Pekarsky, A. I.
openaire   +2 more sources

Phase Engineering of Atomically Precise Nanoclusters (APNCs) of Gold and Beyond

Advanced Materials, EarlyView.
Engineering the structural phase of materials is of paramount importance for both fundamental research and practical applications. In this Review, we summarize the recent progress in controlling the phases of atomically precise nanoclusters (APNCs) of gold, silver and copper, as well as bimetallic systems. The phase‐enabled material properties of APNCs
Yitong Wang, Avirup Sardar, Weijie Ji, Zhongyu Liu, Rongchao Jin   +4 more
wiley   +1 more source

Designing quantum multi-category classifier from the perspective of brain processing information

Machine Learning: Science and Technology
In the field of machine learning, the multi-category classification problem plays a crucial role. Solving the problem has a profound impact on driving the innovation and development of machine learning techniques and addressing complex problems in the ...
Xiaodong Ding, Jinchen Xu, Zhihui Song, Yifan Hou, Zheng Shan   +4 more
doaj   +1 more source

Quantum Cellular Automata for Quantum Error Correction and Density Classification

Physical Review Letters
Quantum cellular automata are alternative quantum-computing paradigms to quantum Turing machines and quantum circuits. Their working mechanisms are inherently automated, therefore measurement free, and they act in a translation invariant manner on all cells/qudits of a register, generating a global rule that updates cell states locally, i.e., based ...
Guedes, T. L. M., Winter, D., Müller, M.   +2 more
openaire   +6 more sources