Towards quantum advantage for topological data analysis

by   Casper Gyurik, et al.

A particularly promising line of quantum machine leaning (QML) algorithms with the potential to exhibit exponential speedups over their classical counterparts has recently been set back by a series of "dequantization" results, that is, quantum-inspired classical algorithms which perform equally well in essence. This raises the important question whether other QML algorithms are susceptible to such dequantization, or whether it can be formally argued that they are out of reach of classical computers. In this paper, we study the quantum algorithm for topological data analysis by Lloyd, Garnerone and Zanardi (LGZ). We provide evidence that certain crucial steps in this algorithm solve problems that are classically intractable by closely relating them to the one clean qubit model, a restricted model of quantum computation whose power is strongly believed to lie beyond that of classical computation. While our results do not imply that the topological data analysis problem solved by the LGZ algorithm (i.e., Betti number estimation) is itself DQC1-hard, our work does provide the first steps towards answering the question of whether it is out of reach of classical computers. Additionally, we discuss how to extend the applicability of this algorithm beyond its original aim of estimating Betti numbers and demonstrate this by looking into quantum algorithms for spectral entropy estimation. Finally, we briefly consider the suitability of the LGZ algorithm for near-term implementations.


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