The Dynamic Time Warping Distance Measure as Q U BO Formulation

Sebastian Feld, Christoph Roch, Thomas Gabor, Xiao-Ting Michelle To, Claudia Linnhoff-Popien
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引用次数: 1

Abstract

Dynamic Time Warping (DTW) is a representative of a distance measure that is able to calculate the distance between two time series. It is often used for the recognition of handwriting or spoken language. The metaheuristic Quantum Annealing (QA) can be used to solve combinatorial optimization problems. Similar to Simulated Annealing it seeks to find a global minimum of a target function. In order to use specialized QA hardware, the problem to be optimized needs to be translated into a Quadratic Unconstrained Binary Optimization (QUBO) problem. With this paper we investigate whether it is possible to transfer the DTW distance measure into a QUBO formulation. The motivation behind is the hope on an accelerated execution once the QA hardware scales up and the aspiration of gaining benefits due to quantum effects that are not given in the classical calculation. In principle, we find that it is possible to transform DTW into a QUBO formulation suitable for executing on QA hardware. Also, the algorithm returns not only the minimum total distance between two sequences, but also the corresponding warping path. However, there are several difficulties that make a manual intervention necessary.
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基于qubo公式的动态时间翘曲距离测度
动态时间翘曲(DTW)是一种能够计算两个时间序列之间距离的距离度量方法的代表。它通常用于识别笔迹或口语。元启发式量子退火(QA)可以用于解决组合优化问题。与模拟退火类似,它寻求找到目标函数的全局最小值。为了使用专用的QA硬件,需要将待优化的问题转化为二次型无约束二进制优化(QUBO)问题。本文探讨了是否有可能将DTW距离度量转换为QUBO公式。背后的动机是,一旦QA硬件扩展,希望能够加速执行,并希望获得经典计算中未给出的量子效应带来的好处。原则上,我们发现可以将DTW转换为适合在QA硬件上执行的QUBO公式。此外,该算法不仅返回两个序列之间的最小总距离,还返回相应的翘曲路径。然而,有几个困难使得人工干预成为必要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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