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.
Adjunct Assistant Professor, Department of Physics and Astronomy, University of Waterloo
Assistant Professor, Status Only, Department of Physics, University of Toronto
Canada CIFAR Artificial Intelligence Chair
Research Interests
- Condensed Matter Physics
- Quantum Computing
- Machine Learning
Highlights
- Member, Acceleration Consortium
- Member of the Centre for Quantum Information and Quantum Control the University of Toronto
- 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
Publications
Probabilistic simulation of quantum circuits using a deep-learning architecture
2021
Protocol discovery for the quantum control of majoranas by differentiable programming and natural evolution strategies
2021
Autoregressive neural network for simulating open quantum systems via a probabilistic formulation
2022
Attention-based quantum tomography
2021
Variational neural annealing
2021
How To Use Neural Networks To Investigate Quantum Many-Body Physics
2021
U (1)-symmetric recurrent neural networks for quantum state reconstruction
2021
Optimal control of quantum thermal machines using machine learning
2022
Neural Error Mitigation of Near-Term Quantum Simulations
2022
Detecting topological order using recurrent neural network wave functions
2022
Machine-learning Augmented Shadow Tomography (Part I)
2022
Machine Learning Augmented Shadow Tomography (Part II)
2022