



01903nas a2200193   4500008004100000245006000041210006000101260001400161520128300175653004301458653002701501653002901528653003401557653003101591100001501622700002001637700001501657856003701672       2022                        eng d00aDifferentiable Quantum Programming with Unbounded Loops0 aDifferentiable Quantum Programming with Unbounded Loops  c11/8/20223 a<p>The emergence of variational quantum applications has led to the development of automatic differentiation techniques in quantum computing. Recently, Zhu et al. (PLDI 2020) have formulated differentiable quantum programming with bounded loops, providing a framework for scalable gradient calculation by quantum means for training quantum variational applications. However, promising parameterized quantum applications, e.g., quantum walk and unitary implementation, cannot be trained in the existing framework due to the natural involvement of unbounded loops. To fill in the gap, we provide the first differentiable quantum programming framework with unbounded loops, including a newly designed differentiation rule, code transformation, and their correctness proof. Technically, we introduce a randomized estimator for derivatives to deal with the infinite sum in the differentiation of unbounded loops, whose applicability in classical and probabilistic programming is also discussed. We implement our framework with Python and Q#, and demonstrate a reasonable sample efficiency. Through extensive case studies, we showcase an exciting application of our framework in automatically identifying close-to-optimal parameters for several parameterized quantum applications.</p>10aFOS: Computer and information sciences10aFOS: Physical sciences10aMachine Learning (cs.LG)10aProgramming Languages (cs.PL)10aQuantum Physics (quant-ph)1 aFang, Wang1 aYing, Mingsheng1 aWu, Xiaodi  uhttps://arxiv.org/abs/2211.04507