The precise control and evaluation of quantum hardware requires a well calibrated model of the dynamical laws governing it. Methods like state and process tomography permit such calibration in principle, but they require a very large number of measurements and dealing with the noise inherent to the hardware makes them fragile. Instead of these methods, we will see how tools borrowed from compressed sensing and machine learning provide for cheaper, more robust, and higher fidelity calibration procedure.
Going one step higher the technology stack, we need to use these calibration techniques to actually prepare non-classical resources for use in quantum computation. One of the most ubiquitous such resource is quantum entanglement. We will see how one can optimize the entanglement distillation circuits for the error model of the actual hardware. The optimized circuits perform substantially better than a general distillation circuit by virtue of being optimized for the particularities of the hardware – this way the results from the previously discussed calibration procedure inform the design of upper layers of the technology stack.
Thesis Advisor: Liang Jiang (liang.jiang@yale.edu)