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In the age of information, data processing and computation are essential. But current computation devices lack scalability and suffer from high carbon emissions. So researchers are constantly looking for alternative architectures and paradigms. Optical (or photonic) computing has emerged as a promising and innovative field with the potential to revolutionize various aspects of computing and information processing. It offers high-speed and parallel computation with significantly less energy consumption. Recently, there has been an increased interest in the field of optical computing and its interdisciplinary applications: platforms, architectures, integrable hardware and protocols for storage, encryption, data and signal processing and computation.
This is a joint collection between Communications Physics and Communications Engineering, with a scope covering the most fundamental to the most applied aspects of the Collection’s topic. The aim of the collection is to create a cross-journal venue to maximize the exposure for the manuscripts across different readerships and communities. The submissions will be handled by the in-house editors and guest editors in each journal independently. The editorial team may recommend a transfer of submissions between journals based on best fit to the journal scope.
We invite researchers from both academia and industry in optical computing to contribute their original research Articles, Reviews, and Perspectives to this Collection. Topics of interest include but are not limited to:
Optical, photonics and quantum neural networks, machine learning, reservoir computing
Optical and photonics logic components, processors and optimisers
Optical, photonics and quantum data storage and encryption
Optical techniques adopted in optical computing rely on spatial multiplexing, requiring numerous integrated elements and restricting the architecture to perform a single kernel convolution per layer. The authors demonstrate a fiber-optic computing architecture based on temporal multiplexing that performs multiple convolutions in a single layer.
Signal processing is key to communications and video image processing for astronomy, medical diagnosis, autonomous driving, big data and AI. Menxi Tan and colleagues report a photonic processor operating at 17Tb/s for ultrafast robotic vision and machine learning.
Bablich and colleagues report an Intrinsic Photomixing Detector (IPD), a cost-effective amorphous silicon device, enabling high-bandwidth, long-distance, and high accuracy Time-of-Flight optical ranging even in low light conditions. The device allows for large-scale integration on silicon or flexible platforms.
Jiashuo Shi and colleagues build an integrated camera capable of tracking objects of interest. They use optical computing to arrange molecules in the liquid crystal mask for enhanced distinction between the object and background.
Stanley Cheung and co-authors introduce co-integrated III-V/Si memristors with fundamental photonic building blocks used in both communication and computing applications. This allows a path towards realizing low-loss, non-volatile optical elements with near-zero static power consumption.
Optical beams carrying orbital angular momentum (OAM) are promising candidates for free-space optical communication. The authors devise a hybrid optical-electronic convolutional neural network approach reaching a 4-bit OAM-coded signal demultiplexing accuracy of 72.84% under strong atmospheric turbulence conditions with 3.2 times faster training time than all electronic convolutional neural network.
On-chip photonic neural network processors speed up image processing, but their scalability is limited by the number of input/output channels. Here, the authors develop a scalable image processing approach based on the compressive acquisition of real-world visual information via a single-input channel, and apply their setup as a compressive temporal encoder for highspeed imaging.