Chinese researchers have now proposed a digital twin model that fundamentally changes how these complex systems are developed and tested.<\/p>\n
Latest Videos From Traditional optical computing systems face a persistent challenge, since task development relies heavily on direct access to physical hardware platforms.<\/p>\n When multiple researchers need to work with the same system, they typically wait in line, then repeatedly tune parameters and perform error calibration before any genuine computation can begin.<\/p>\n You may like<\/p>\n Once one user finishes, the next often must readjust the entire system state, making parallel research nearly impossible across competing projects.<\/p>\n That cycle of waiting, tuning, and recalibrating drives up trial-and-error costs while severely limiting overall research efficiency.<\/p>\n To address that bottleneck, researchers developed what they call the Digital Twin Optical Computing System, or DT-OCS, published in Opto-Electronic Advances.<\/p>\n The framework constructs a digital model that reproduces the input-output responses of a physical optical computing system across different configuration parameters entirely within software.<\/p>\n If the physical system resembles an expensive, heavily occupied real machine, researchers describe DT-OCS as functioning like a high-fidelity simulator running alongside it.<\/p>\n What to read next<\/p>\n Testing image classification inside a virtual twin before touching real hardware<\/p>\n Using a high-speed optical computing system paired with a silicon photonic feature-computing chip, the research team tested DT-OCS on image classification and sequential decision-making tasks.<\/p>\n The results showed that configuration parameters trained and optimized within the digital twin transferred directly to the physical system without requiring further adjustment.<\/p>\n Task performance on the physical hardware matched the digital model’s predictions closely, validating both the fidelity and transferability of the entire approach.<\/p>\n Because training and optimization happen primarily within the digital domain, researchers can now develop multiple distinct tasks simultaneously rather than queuing for shared hardware access.<\/p>\n The team has also made the DT-OCS framework and its associated datasets openly available.<\/p>\n This will allow other researchers to conduct training and validation without ever touching physical equipment themselves.<\/p>\n According to the researchers, they designed DT-OCS as \u201ca reproducible, accessible, and scalable software resource for wider sharing and validation.\u201d<\/p>\n The openness effectively transforms optical computing from a specialized resource constrained by device availability into something closer to a shareable, reproducible research platform.<\/p>\n The researchers argue that future optical computing systems should pair physical hardware with openly available digital models offering equivalent computational behaviour.<\/p>\n Drawing a comparison to how modern transportation depends on both physical roads and continuously updated digital maps, they suggest mature optical computing platforms need a similar dual structure going forward.<\/p>\n Via EurekAlert<\/a><\/p>\n \n Follow TechRadar on Google News<\/a> and add us as a preferred source<\/a> to get our expert news, reviews, and opinion in your feeds. <\/p>\n","protected":false},"excerpt":{"rendered":"Optical computing uses light instead of electricity to process complex data.Digital twin eliminates long waits for shared 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Why physical hardware became a bottleneck for researchers<\/p>\n![\"Google](\"https:\/\/www.newsbeep.com\/il\/wp-content\/uploads\/2026\/04\/diM9tpwF2Lz85R8q85CT78.jpg\")
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