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Scientists Develop First Quantum Algorithm to Characterize Noise Across Large Systems

IBM Quantum
Researchers say they developed rigorous and scalable diagnostic algorithms capable of being run on current quantum devices and beyond.

Noise is the main obstacle to building large-scale quantum computers.

To tame the noise (interference or instability), scientists need to understand how it affects an entire quantum system. Until now this information was only available for very small devices or subsets of devices.

Dr Robin Harper and colleagues, who recently published their work in Nature Physics, report that they developed algorithms that will work across large quantum devices.

According to a news release the team demonstrated this by diagnosing the noise in an IBM Quantum Experience device, discovering correlations in the 14-qubit machine not previously detected.

“The results are the first implementation of provably rigorous and scalable diagnostic algorithms capable of being run on current quantum devices and beyond,” said Harper, who is a postdoctoral researcher at the University of Sydney Nano Institute and part of the Australian Research Council Centre of Excellence for Engineered Quantum Systems.

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This work was supported in part by the U.S. Army Research Office, the Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUS), the Government of Ontario, and the Government of Canada through the Canada First Research Excellence Fund (CFREF) and Transformative Quantum Technologies (TQT), the Natural Sciences and Engineering Research Council (NSERC), Industry Canada.

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Matt Swayne

With a several-decades long background in journalism and communications, Matt Swayne has worked as a science communicator for an R1 university for more than 12 years, specializing in translating high tech and deep tech for the general audience. He has served as a writer, editor and analyst at The Quantum Insider since its inception. In addition to his service as a science communicator, Matt also develops courses to improve the media and communications skills of scientists and has taught courses. [email protected]

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The Future of Materials Discovery: Reducing R&D Costs significantly with GenMat’s AI and Machine Learning Tools

When: July 13, 2023 at 11:30am

What: GenMat Webinar

Picture of Jake Vikoren

Jake Vikoren

Company Speaker

Picture of Deep Prasad

Deep Prasad

Company Speaker

Picture of Araceli Venegas

Araceli Venegas

Company Speaker

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