Quantum Algorithm for Estimating Volumes of Convex Bodies

Shouvanik Chakrabarti, Andrew M. Childs, S. Hung, Tongyang Li, C. Wang, Xiaodi Wu
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引用次数: 14

Abstract

Estimating the volume of a convex body is a central problem in convex geometry and can be viewed as a continuous version of counting. We present a quantum algorithm that estimates the volume of an n-dimensional convex body within multiplicative error ε using Õ(n3 + n2.5/ε) queries to a membership oracle and Õ(n5+n4.5/ε) additional arithmetic operations. For comparison, the best known classical algorithm uses Õ(n3.5+n3/ε2) queries and Õ(n5.5+n5/ε2) additional arithmetic operations. To the best of our knowledge, this is the first quantum speedup for volume estimation. Our algorithm is based on a refined framework for speeding up simulated annealing algorithms that might be of independent interest. This framework applies in the setting of “Chebyshev cooling,” where the solution is expressed as a telescoping product of ratios, each having bounded variance. We develop several novel techniques when implementing our framework, including a theory of continuous-space quantum walks with rigorous bounds on discretization error. To complement our quantum algorithms, we also prove that volume estimation requires Ω (√ n+1/ε) quantum membership queries, which rules out the possibility of exponential quantum speedup in n and shows optimality of our algorithm in 1/ε up to poly-logarithmic factors.
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凸体体积估计的量子算法
估计凸体的体积是凸几何中的一个中心问题,可以看作是计数的一个连续版本。我们提出了一种量子算法,该算法使用Õ(n3 + n2.5/ε)对隶属性oracle的查询和Õ(n5+n4.5/ε)额外的算术运算来估计n维凸体在乘法误差ε内的体积。相比之下,最著名的经典算法使用Õ(n3.5+n3/ε2)查询和Õ(n5.5+n5/ε2)附加算术运算。据我们所知,这是体积估计的第一个量子加速。我们的算法是基于一个改进的框架来加速模拟退火算法,这可能是独立的兴趣。这个框架适用于“切比雪夫冷却”的设置,其中解被表示为比率的伸缩乘积,每个比率都有有限的方差。在实现我们的框架时,我们开发了几种新技术,包括具有严格离散误差界限的连续空间量子行走理论。为了补充我们的量子算法,我们还证明了体积估计需要Ω(√n+1/ε)量子隶属度查询,这排除了n的指数量子加速的可能性,并显示了我们的算法在1/ε到多对数因子的最优性。
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