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Differentiable Analog Quantum Computing for Optimization and Control
Jiaqi Leng · Yuxiang Peng · Yi-Ling Qiao · Ming Lin · Xiaodi Wu

Tue Dec 06 05:00 PM -- 07:00 PM (PST) @
in Lightning Talks 2B-2 »

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}.

Author Information

Jiaqi Leng (University of Maryland, College Park)
Yuxiang Peng (University of Maryland, College Park)
Yi-Ling Qiao (University of Maryland, College Park)
Ming Lin (University of Maryland - College Park)

Ming C. Lin is currently the Elizabeth Stevinson Iribe Chair of Computer Science at the University of Maryland College Park and John R. & Louise S. Parker Distinguished Professor Emerita of Computer Science at the University of North Carolina (UNC), Chapel Hill. She was also an Honorary Visiting Chair Professor at Tsinghua University in China and at University of Technology Sydney in Australia. She obtained her B.S., M.S., and Ph.D. in Electrical Engineering and Computer Science from the University of California, Berkeley. She received several honors and awards, including the NSF Young Faculty Career Award in 1995, Honda Research Initiation Award in 1997, UNC/IBM Junior Faculty Development Award in 1999, UNC Hettleman Award for Scholarly Achievements in 2003, Beverly W. Long Distinguished Professorship 2007-2010, UNC WOWS Scholar 2009-2011, IEEE VGTC Virtual Reality Technical Achievement Award in 2010, and many best paper awards at international conferences. She is a Fellow of ACM, IEEE, and Eurographics.

Xiaodi Wu (University of Maryland)

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