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Creators/Authors contains: "Qiao, Yi-Ling"

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  1. ; ; ; ; ; (, Advances in Neural Information Processing Systems)
    Globerson, A; Mackey, L; Belgrave, D; Fan, A; Paquet, U; Tomczak, J; Zhang, C (Ed.)
    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ; (, International Conference on Learning Representations (ICLR))
    Accepted Manuscript Full Text Available
  3. ; ; ; ; (, Advances in neural information processing systems)
    We formulate the first differentiable analog quantum computing framework with specific parameterization design at the analog signal (pulse) level to better exploit near-term quantum devices via variational methods. We further propose a scalable approach to estimate the gradients of quantum dynamics using a forward pass with Monte Carlo sampling, which leads to a quantum stochastic gradient descent algorithm for scalable gradient-based training in our framework. Applying our framework to quantum optimization and control, we observe a significant advantage of differentiable analog quantum computing against SOTAs based on parameterized digital quantum circuits by {\em orders of magnitude}. 
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    Accepted Manuscript Full Text Available