How Hard Is It to Train Variational Quantum Circuits?

          @misc{ scivideos_15413,
            doi = {},
            url = {https://simons.berkeley.edu/talks/tbd-133},
            author = {},
            keywords = {},
            language = {en},
            title = {How Hard Is It to Train Variational Quantum Circuits?},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2020},
            month = {feb},
            note = {15413 see, \url{https://scivideos.org/Simons-Institute/15413}}
          }
          

Abstract

Variational Quantum Circuits, which include examples of quantum approximate optimization algorithms (QAOA),  variational quantum eigensolver (VQE), and quantum neural-networks (QNN), are predicted to be one of the most important near-term quantum applications, not only because of their potential promises as classical neural-networks but also because of their feasibility on near-term noisy intermediate size quantum (NISQ) machines. This talk reports some progress toward a principled understanding of the training of variational quantum circuits. First, I will demonstrate the difficulty of training by constructing an example with exponentially many local optima,  however,  due to a differential nature from classical neural-networks. Second, I will explain how to facilitate the training by incorporating the optimal-transport norm in the context of quantum generative adversarial networks (GANs), as well as its application in compressing quantum circuits in practical uses.