numerical simulation

Zohreh Davoudi is collaborating with experts in quantum computing technologies to ensure that the relevant problems in her fields of nuclear and particle physics are poised to reap the benefits when quantum simulations mature. Davoudi along with JQI Fellow Alexey Gorshkov and other colleagues proposed a quantum simulation that might be possible to implement soon. They proposal involves using superconducting circuits to simulate a simplified model of collisions between fundamental particles called quarks and mesons (which are themselves made of quarks and antiquarks).

Machine learning, the field that’s driving a revolution in artificial intelligence, has cemented its role in modern technology. Its tools and techniques have led to rapid improvements in everything from self-driving cars and speech recognition to the digital mastery of an ancient board game.Now, physicists are beginning to use machine learning tools to tackle a different kind of problem, one at the heart of quantum physics. In a paper published recently in Physical Review X, researchers from JQI and the Condensed Matter Theory Center (CMTC) at the University of Maryland showed that certain neural networks—abstract webs that pass information from node to node like neurons in the brain—can succinctly describe wide swathes of quantum systems.