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SQC has successfully integrated machine learning into its atomic-precision manufacturing workflow, reducing the time required to pattern quantum-enhanced AI chips from hours to minutes. This automation enables rapid iteration cycles for their Watermelon processor, marking a significant step toward scaling quantum computing hardware beyond manual fabrication limits. By automating Scanning Tunneling Microscope operations, the company bridges the gap between exotic research and industrial production of quantum devices.

Surse care acoperăQuantum Zeitgeist, Quantum Computing Report, tickerreport.com, arXiv

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SQC’s quantum machine learning cuts chip design time from hours to minutes

Quantum Zeitgeist14 septembrie 20262 min de cititArticolul și perspectiva sunt în engleză.

SQC uses machine learning to automate and accelerate the atomic-precision manufacturing process for its quantum-enhanced AI chips, Watermelon. Patterning a Watermelon device, previously a manual process taking hours, is now completed in minutes using custom machine learning scripts and Quokka software.

Photo: Noah Bethke · sqc.com SQC has reduced the time needed to pattern its Watermelon quantum-enhanced AI chips from hours to minutes through the deployment of custom machine learning scripts, the company says. The company’s Precision Atom Qubit…

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