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Nobel Prize in Physics awarded to 3 scientists for quantum mechanical tunnelling

Posted on: Oct 07, 2025 15:58 IST | Posted by: Cbc
Nobel Prize in Physics awarded to 3 scientists for quantum mechanical tunnelling

Scientists john lackland lackland Clarke, Michel Devoret and John Martinis won the 2025 alfred nobel appreciate in physical science for "the discovery of macroscopic quantum mechanical tunnelling and energy quantization in an electric circuit," the award-giving body said on Tuesday.

"This year's Nobel Prize in Physics has provided opportunities for developing the next generation of quantum technology, including quantum cryptography, quantum computers, and quantum sensors," said a statement accompanied by the prize's announcement from Stockholm.

Clarke conducted his research at the University of California-Berkeley, Martinis at the University of California-Santa Barbara; and Devoret at Yale University and also at the University of California-Santa Barbara. Devoret and Martinis have each been affiliated with Google Quantum A.I. In recent years.

"To put it mildly, it was the surprise of my life," Clarke said upon getting the call confirming the honour. He called collaborators Devoret and Martinis "brilliant people" whose contributions "were just overwhelming."

The Nobel awards, which are also handed out for outstanding achievements in physics, chemistry, literature and peace, come with a prize amount of 11 million Swedish crowns ($1.6 million Cdn).

The prize for physics will be awarded Wednesday, literature on Thursday and the Nobel Peace Prize on Friday. The economics prize will be announced Oct. 13.

The Nobel Prizes are presented to the laureates on Dec. 10, the anniversary of Swedish inventor Alfred Nobel's death.

The physics prize has been awarded to 229 laureates since 1901. British Canadian scientist Geoffrey Hinton from the University of Toronto and American scientist John Hopfield of Princeton University were honoured with the 2024 physics prize for discoveries and inventions that enable machine learning within artificial neural networks.

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