Juan Felipe Carrasquilla Álvarez

Faculty Member

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

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.

Research Interests

  • Condensed Matter Physics
  • Quantum Computing
  • Machine Learning


  • 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


Probabilistic simulation of quantum circuits using a deep-learning architecture

Juan Carrasquilla and Di Luo and Felipe Pérez and Ashley Milsted and Bryan K Clark and Maksims Volkovs and Leandro Aolita


Protocol discovery for the quantum control of majoranas by differentiable programming and natural evolution strategies

Luuk Coopmans and Di Luo and Graham Kells and Bryan K Clark and Juan Carrasquilla


Autoregressive neural network for simulating open quantum systems via a probabilistic formulation

Di Luo and Zhuo Chen and Juan Carrasquilla and Bryan K Clark


Attention-based quantum tomography

Peter Cha and Paul Ginsparg and Felix Wu and Juan Carrasquilla and Peter L McMahon and Eun-Ah Kim


Variational neural annealing

Mohamed Hibat-Allah and Estelle M Inack and Roeland Wiersema and Roger G Melko and Juan Carrasquilla


How To Use Neural Networks To Investigate Quantum Many-Body Physics

Juan Carrasquilla and Giacomo Torlai


U (1)-symmetric recurrent neural networks for quantum state reconstruction

Stewart Morawetz and Isaac JS De Vlugt and Juan Carrasquilla and Roger G Melko


Optimal control of quantum thermal machines using machine learning

Ilia Khait and Juan Carrasquilla and Dvira Segal


Neural Error Mitigation of Near-Term Quantum Simulations

Florian Hopfmueller and Elizabeth Bennewitz and Bohdan Kulchytskyy and Juan Carrasquilla and Pooya Ronagh


Detecting topological order using recurrent neural network wave functions

Mohamed Hibat-Allah and Roger Melko and Juan Carrasquilla


Machine-learning Augmented Shadow Tomography (Part I)

Peter Cha and Tim Skaras and Robert Huang and Juan Carrasquilla and Peter McMahon and Eun-Ah Kim


Machine Learning Augmented Shadow Tomography (Part II)

Timothy Skaras and Peter Cha and Robert Huang and Juan Carrasquilla and Peter McMahon and Eun-Ah Kim