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arXiv · 1409.1524

Quantum Bootstrapping via Compressed Quantum Hamiltonian Learning

Nathan Wiebe·Christopher Granade·David G. Cory
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Abstract

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.

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Nathan Wiebe, Christopher Granade, David G. Cory. 2015-03-30. Quantum Bootstrapping via Compressed Quantum Hamiltonian Learning. https://doi.org/10.1088/1367-2630%2F17%2F2%2F022005

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