Click here to log in with

or

Forgot Password?

Learn more

July 29, 2021

from the University of Sydney

Researchers at the University of Sydney and the quantum control startup Q-CTRL today announced a method for identifying sources of error in quantum computers through machine learning, which enables hardware developers to locate degraded performance with unprecedented accuracy and to find ways to useful quantum computers accelerate.

googletag.cmd.push (function () {googletag.display (‘div-gpt-ad-1449240174198-2’);});

A joint scientific article titled “Quantum Oscillator Noise Spectroscopy via Displaced Cat States” was published in Physical Review Letters, the world’s leading physical science research journal and flagship publication of the American Physical Society (APS Physics).

Focused on reducing errors caused by environmental “noise” – the Achilles’ heel of quantum computing – the University of Sydney team developed a technique to detect the smallest deviations from the exact conditions required for the Execution of quantum algorithms with trapped ion and superconducting quantum computing hardware are required. These are the core technologies used by the world’s leading industrial quantum computing efforts at IBM, Google, Honeywell, IonQ, and others.

In order to localize the source of the measured deviations, the Q-CTRL scientists developed a new method for processing the measurement results with user-defined algorithms for machine learning. When combined with Q-CTRL’s existing quantum control techniques, the researchers were also able to minimize the effects of background interference on the process. This enabled an easy distinction between “real” noise sources that could be fixed and phantom artifacts from the measurements themselves.

“The combination of cutting edge experimental techniques with machine learning has shown enormous advantages in the development of quantum computers,” said Dr. Cornelius Hempel from ETH Zurich, who headed research at the University of Sydney. “The Q-CTRL team was able to quickly develop a professionally developed machine learning solution that enabled us to understand our data and provide a new way of ‘seeing’ and addressing the problems in the hardware.”

Q-CTRL CEO and Professor at Sydney University Michael J. Biercuk said, “The ability to identify and suppress sources of degradation in quantum hardware is beneficial to both basic research and industrial construction efforts of quantum sensors and quantum computers is crucial.

“Quantum control, complemented by machine learning, has shown a way to make these systems workable and to dramatically speed up the R&D schedules,” he said.

“The published results in a prestigious, from Peer-reviewed journals confirm the benefits of ongoing collaboration between basic scientific research in a university laboratory and deep-tech startups. We look forward to advancing the field through our cooperation. ”

Use this form if you have discovered a typographical error, inaccuracy, or if you would like to submit a change request to the contents of this page.
For general inquiries, please use our contact form.
For general feedback, use the public comments section below (please follow the guidelines).

Your feedback is important to us. Due to the high volume of messages, however, we cannot guarantee individual responses.

Your email address will only be used to let the recipient know who sent the email. Neither your address nor that of the recipient will be used for any other purpose.
The information you entered will appear in your email message and will not be stored in any form by Phys.org.

Receive weekly and / or daily updates in your inbox.
You can unsubscribe at any time and we will never pass your data on to third parties.

This website uses cookies to make navigation easier, to analyze your use of our services and to provide third-party content.
By using our website, you confirm that you have read and understood our privacy policy
and terms of use.

Similar title :
Q-CTRL and Sydney Uni in Quantum Control Progress
Machine Learning to Detect Quantum Defects
AI was first used to locate quantum defects

Ref: https://phys.org