Quantum Bootstrapping via Compressed Quantum Hamiltonian Learning

Nathan Wiebe1, Chris Granade2, 3 and D. G. Cory2,4,5


Presented as a talk at LFQIS 2014, and at the University of Sydney. Presented as a poster at QIP 2015.


Recent work has shown that quantum simulation is a valuable tool for learning empirical models for quantum systems. We build upon these results by showing that a small quantum simulators can be used to characterize and learn control models for larger devices for wide classes of physically realistic Hamiltonians. This leads to a new application for small quantum computers: characterizing and controlling larger quantum computers. Our protocol achieves this by using Bayesian inference in concert with Lieb-Robinson bounds and interactive quantum learning methods to achieve compressed simulations for characterization. Whereas Fisher information analysis shows that current methods which employ short-time evolution are suboptimal, interactive quantum learning allows us to overcome this limitation. We illustrate the efficiency of our bootstrapping protocol by showing numerically that an 8-qubit Ising model simulator can be used to calibrate and control a 50 qubit Ising simulator while using only about 750 kilobits of experimental data.

Software Resources

QInfer, a Python-language implementation of the classical portions of the algorithm presented in this work, is available from GitHub.


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  1. Microsoft Research.
  2. Institute for Quantum Computing, University of Waterloo.
  3. Department of Physics, University of Waterloo.
  4. Department of Chemistry, University of Waterloo.
  5. Perimeter Institute for Theoretical Physics.