New Delhi: Three scientists from the US have been conferred the 2025 #NobelPrize in Physics for their experiments on a chip that revealed quantum physics in action.
The prestigious prize has provided opportunities for developing the next generation of quantum technology, including quantum cryptography, quantum computers, and quantum sensors.
Two scientists are from the University of California, Berkeley and Santa Barbara — John Clarke and John M. Martinis. Michel H. Devoret is from Yale University, New Haven, and the University of California, Santa Barbara. The trio will share the prize money of 11 million Swedish kronor.
“The 2025 #NobelPrize in Physics has been awarded to John Clarke, Michel H. Devoret, and John M. Martinis for the discovery of macroscopic quantum mechanical tunnelling and energy quantisation in an electric circuit,” the Royal Swedish Academy of Sciences said.
Quantum mechanics allows a particle to move straight through a barrier, using a process called tunnelling. As soon as large numbers of particles are involved, quantum mechanical effects usually become insignificant.
The laureates’ experiments demonstrated that quantum mechanical properties can be made concrete on a macroscopic scale.
They conducted experiments with an electrical circuit in which they demonstrated both quantum mechanical tunnelling and quantised energy levels in a system big enough to be held in the hand.
“It is wonderful to be able to celebrate the way that century-old quantum mechanics continually offers new surprises. It is also enormously useful, as quantum mechanics is the foundation of all digital technology,” said Olle Eriksson, Chair of the Nobel Committee for Physics.
The transistors in computer microchips are one example of the established quantum technology that surrounds us.
The Royal Swedish Academy of Sciences awarded the 2024 Nobel Prize in Physics to John J. Hopfield and Geoffrey E. Hinton for pioneering research in machine learning.
The scientists received the award “for foundational discoveries and inventions that enable machine learning with artificial neural networks”.
(IANS)
