Research Interests

• Condensed matter physics
• Quantum computing
• Machine learning

Biography

Juan’s research interests are at the intersection of condensed matter physics, quantum computing, and machine learning. Juan combines quantum Monte Carlo simulations and machine learning techniques to analyze the collective behaviour of quantum many-body systems. Applications of these ideas include the identification of phases of matter in numerical simulations and experiments, as well as the validation of near-term quantum devices and quantum simulations of condensed matter systems. He completed his PhD in Physics at SISSA, the International School for Advanced Studies in Italy. He has since held positions as a Postdoctoral Fellow at Georgetown University, Visiting Research Scholar at Penn State University, Postdoctoral Fellow at the Perimeter Institute, and  a Research Scientist at D-Wave Systems Inc. Juan has been at the Vector Institute since 2017.

Highlights

• Perimeter Institute Visiting Fellow. December 2017— Present.
• Perimeter Institute Postdoctoral fellowship. 2013-2016
• Georgetown University postdoctoral fellowship. 2011-2013
• International School for Advanced studies PhD fellowship. 2006-2010
• The Abdus Salam ICTP Diploma programme fellowship. 2005-2006

Research Activity and News

Revitalized the connection between the areas of computer vision and the theory of strongly correlated many-body systems. We showed that neural networks have the ability to learn representations of ordered and topologically ordered states of matter. Read more

Used Restricted Boltzmann machines (RBMs) to perform quantum state tomography in systems of unprecedented size. Read more

Performed a large quantum simulation of frustrated magnetism. Read more

• The quantum distillery
• Physicists Teach AI to Identify Exotic States of Matter
• Intelligent Machines Teaching Themselves Quantum    
• Juan Felipe Carrasquilla: Q&A: A Condensed Matter Theorist Embraces AI
• AI Stories of success at ICTP
• Enter the machine Artificial intelligence teaches itself to solve gnarly quantum challenges  
• Artificial Intelligence Techniques Reconstruct Mysteries of Quantum Systems
• Optimizing the synergy between physics and machine learning
• A neural network-based optimization technique inspired by the principle of annealing
• Vector researchers use ML to build better quantum computers
• Quantum tech startup YiyaniQ is Vector’s first spin-out company