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Photonic processors use optical signals for computation, leveraging the high bandwidth and low loss of optical links. While many approaches have been proposed, including in memory photonic circuits, most efforts have focused on the physical…

Emerging applications of photonics in computing, sensing, and security increasingly demand complex input-output behaviors, including highly nonlinear transformations of optical signals. Traditional photonic systems rely on highly structured…

Physics and computer science have a long tradition of cross-fertilization. One of the latest outcomes of this mutually beneficial relationship is quantum information science, which comprises the study of information processing tasks that…

Quantum Physics · Physics 2020-06-17 Fei Yan , Salvador E. Venegas-Andraca , Kaoru Hirota

Recently proposed modifications of the standard particle-in-cell (PIC) method resolve long-standing limitations such as exact preservation of physically conserved quantities and unbiased ensemble down-sampling. Such advances pave the way…

Plasma Physics · Physics 2026-03-13 Frida Brogren , Christoffer Olofsson , Joel Magnusson , Arkady Gonoskov

Software-implementation, via neural networks, of brain-inspired computing approaches underlie many important modern-day computational tasks, from image processing to speech recognition, artificial intelligence and deep learning…

Optics · Physics 2021-02-19 J. Feldmann , N. Youngblood , C. D. Wright , H. Bhaskaran , W. H. P. Pernice

The implicit 2D3V particle-in-cell (PIC) code developed to study the interaction of ultrashort pulse lasers with matter [G. M. Petrov and J. Davis, Computer Phys. Comm. 179, 868 (2008); Phys. Plasmas 18, 073102 (2011)] has been parallelized…

Plasma Physics · Physics 2016-08-24 George M. Petrov , Jack Davis

Photonic computing promises ultrafast and energy-efficient artificial intelligence. However, existing photonic neural networks (PNNs) remain functionally shallow and difficult to scale. Here we establish a theory-guided framework showing…

Optics · Physics 2026-02-25 Yuxin Sun , Chun Gao , Jin Xie , Pan Wang , Zejie Yu , Yiwei Xie , Huan Li , Daoxin Dai

Recent advances of quantum technologies rely on precise control and integration of quantum objects, and technological breakthrough is anticipated for further scaling up to realize practical applications. Trapped-ion quantum technology is a…

Quantum Physics · Physics 2025-09-03 Alto Osada , Koichiro Miyanishi

Ising machines are an emerging class of hardware that promises ultrafast and energy-efficient solutions to NP-hard combinatorial optimization problems. Spatial photonic Ising machines (SPIMs) exploit optical computing in free space to…

Speech recognition is a critical task in the field of artificial intelligence and has witnessed remarkable advancements thanks to large and complex neural networks, whose training process typically requires massive amounts of labeled data…

Neural and Evolutionary Computing · Computer Science 2024-05-24 Enrico Picco , Alessandro Lupo , Serge Massar

The separation of the data capture and analysis in modern vision systems has led to a massive amount of data transfer between the end devices and cloud computers, resulting in long latency, slow response, and high power consumption.…

Image and Video Processing · Electrical Eng. & Systems 2024-08-13 Ruibing Song , Kejie Huang , Zongsheng Wang , Haibin Shen

Photonic chips that integrate guides, switches, gratings and other components, process vast amounts of information rapidly on a single device. A new branch of this technology becomes possible if the light is coupled to cold atoms in a…

Quantum Physics · Physics 2010-11-23 M. Kohnen , M. Succo , P. G. Petrov , R. A. Nyman , M. Trupke , E. A. Hinds

Analog computing using bosonic computational states is a leading approach to surpassing the computational speed and energy limitations of von Neumann architectures. But the challenges of manufacturing large-scale photonic integrated…

Optics · Physics 2025-07-22 Farhad Khosravi , Martin Perreault , Artur Scherer , Pooya Ronagh

The advances in fabrication processes in different material platforms employed in integrated optics are opening the path towards the implementation of circuits with increasing degree of complexity. In addition to the more conventional…

Applied Physics · Physics 2018-10-24 Daniel Perez , Jose Capmany

We propose advancing photonic in-memory computing through three-dimensional photonic-electronic integrated circuits using phase-change materials (PCM) and AlGaAs-CMOS technology. These circuits offer high precision (greater than 12 bits),…

Neuromorphic computing is an emerging research field that aims to develop new intelligent systems by integrating theories and technologies from multi-disciplines such as neuroscience and deep learning. Currently, there have been various…

Neural and Evolutionary Computing · Computer Science 2022-07-27 Chaofei Hong , Mengwen Yuan , Mengxiao Zhang , Xiao Wang , Chegnjun Zhang , Jiaxin Wang , Gang Pan , Zhaohui Wu , Huajin Tang

Integrated quantum photonics offers a promising path to scale up quantum optics experiments by miniaturizing and stabilizing complex laboratory setups. Central elements of quantum integrated photonics are quantum emitters, memories,…

Programmable integrated photonics (PIP) has emerged as a powerful on-chip platform for optical signal processing and computing, enabling the implementation of reconfigurable N$\times$N unitary matrix transformations through meshes of…

We show optical waves passing through a nanophotonic medium can perform artificial neural computing. Complex information, is encoded in the wave front of an input light. The medium transforms the wave front to realize sophisticated…

Optics · Physics 2019-06-11 Erfan Khoram , Ang Chen , Dianjing Liu , Lei Ying , Qiqi Wang , Ming yuan , Zongfu Yu

Deep learning has redefined the field of artificial intelligence (AI) thanks to the rise of artificial neural networks, which are architectures inspired by their neurological counterpart in the brain. Through the years, this dualism between…

Machine Learning · Computer Science 2023-02-21 Tommaso Salvatori , Yuhang Song , Thomas Lukasiewicz , Rafal Bogacz , Zhenghua Xu