Juan Carrasquilla is a Canada CIFAR AI Chair at the Vector Institute and an adjunct assistant professor at the department of Physics & Astronomy at the University of Waterloo
Carrasquilla’s research interests are at the intersection of condensed matter physics, quantum computing, and machine learning. He 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.
- Perimeter Institute Visiting Fellow, 2017— present
- Perimeter Institute Postdoctoral Fellow, 2013-2016
- Georgetown University Postdoctoral Fellow, 2011-2013
- International School for Advanced studies PhD Fellow, 2006-2010
- The Abdus Salam ICTP Diploma Programme Fellow, 2005-2006
Hibat-Allah, M., Ganahl, M., Hayward, L. E., Melko, R. G., & Carrasquilla, J. (2020). Recurrent neural network wave functions. Physical Review Research, 2(2), 023358.
Carrasquilla, J. (2020). Machine learning for quantum matter. Advances in Physics: X, 5(1), 1797528.
Carrasquilla, J., Torlai, G., Melko, R. G., & Aolita, L. (2019). Reconstructing quantum states with generative models. Nature Machine Intelligence, 1(3), 155-161.
Melko, R. G., Carleo, G., Carrasquilla, J., & Cirac, J. I. (2019). Restricted Boltzmann machines in quantum physics. Nature Physics, 15(9), 887-892.
Carrasquilla, J., & Melko, R. G. (2017). Machine learning phases of matter. Nature Physics, 13(5), 431-434.
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.