Characterizing and Verifying 100-qubit Scale Quantum Computers

Christopher Granade1, 2, joint work with Nathan Wiebe3, Christopher Ferrie4, Ian Hincks1, 5, Rahul Deshpande1, 2, and D. G. Cory1,6,7

Primarily based on arxiv:1207.1655, arXiv:1309.0876 and arxiv:1311.5269.

Presented 26 February, 2014 as a seminar at CQuIC.

Slides: HTML IPython Notebook


Several challenges stand in the way of developing useful computation at the 100-qubit scale. Current methods for the characterization of large quantum devices, for instance, demand exponentially large resource costs with the number of qubits. Moreover, once a candidate for such small-scale quantum processors is developed, verifying and certifying its dynamics is beyond the scale of what can be achieved using purely classical resources. In this talk, I will argue that both of these problems can be addressed by using classical statistical inference techniques to reduce them to problems of quantum simulation. In particular, I will introduce the classical methods that we build upon, show how quantum simulation and communication can be applied as resources, and will describe experiments in progress that demonstrate the utility of these techniques.

Software Resources

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


View on Zotero

[RG+13] R. Blume-Kohout, J. K. Gamble, E. Nielsen, J. Mizrahi, J. D. Sterk, and P. Maunz, “Robust, self-consistent, closed-form tomography of quantum logic gates on a trapped ion qubit,” arXiv:1310.4492 [quant-ph], Oct. 2013.

[BH+03] N. Boulant, T. F. Havel, M. A. Pravia, and D. G. Cory, “Robust method for estimating the Lindblad operators of a dissipative quantum process from measurements of the density operator at multiple time points,” Phys. Rev. A, vol. 67, no. 4, p. 042322, Apr. 2003.

[dSLP11] M. P. da Silva, O. Landon-Cardinal, and D. Poulin, “Practical Characterization of Quantum Devices without Tomography,” Phys. Rev. Lett., vol. 107, no. 21, p. 210404, Nov. 2011.

[FG13] C. Ferrie and C. E. Granade, “Likelihood-free quantum inference: tomography without the Born Rule,” arXiv e-print 1304.5828, Apr. 2013.

[GFWC12] C. E. Granade, C. Ferrie, N. Wiebe, and D. G. Cory, “Robust online Hamiltonian learning,” New Journal of Physics, vol. 14, no. 10, p. 103013, Oct. 2012.

[HW12] M. J. W. Hall and H. M. Wiseman, “Does Nonlinear Metrology Offer Improved Resolution? Answers from Quantum Information Theory,” Phys. Rev. X, vol. 2, no. 4, p. 041006, Oct. 2012.

[MGE12] E. Magesan, J. M. Gambetta, and J. Emerson, “Characterizing Quantum Gates via Randomized Benchmarking,” Physical Review A, vol. 85, no. 4, Apr. 2012.

[WG+12a] N. Wiebe, C. Granade, C. Ferrie, and D. G. Cory, “Hamiltonian Learning and Certification Using Quantum Resources,” arXiv e-print 1309.0876, Sep. 2013.

[WG+12b] N. Wiebe, C. Granade, C. Ferrie, and D. G. Cory, “Quantum Hamiltonian Learning Using Imperfect Quantum Resources,” arXiv:1311.5269 [quant-ph], Nov. 2013.


  1. Institute for Quantum Computing, University of Waterloo.
  2. Department of Physics, University of Waterloo.
  3. Microsoft Research.
  4. Center for Quantum Information and Control, University of New Mexico.
  5. Department of Applied Mathematics, University of Waterloo.
  6. Department of Chemistry, University of Waterloo.
  7. Perimeter Institute for Theoretical Physics.