Complexity-Theoretic Limitations on Quantum Algorithms for Topological Data Analysis

Abstract

Quantum algorithms for topological data analysis (TDA) seem to provide an exponential advantage over the best classical approach while remaining immune to dequantization procedures and the data-loading problem. In this paper, we argue that quantum algorithms for TDA run in exponential time for almost all inputs by showing that (under widely believed complexitytheoretic conjectures) the central problem of TDA -estimating Betti numbers -is intractable even for quantum computers. Specifically, we prove that the problem of computing Betti numbers exactly is #P-hard, while the problem of approximating Betti numbers up to multiplicative error is NP-hard. Moreover, both problems retain their hardness if restricted to the regime where quantum algorithms for TDA perform best. Because quantum computers are not expected to solve #P-hard or NP-hard problems in subexponential time, our results imply that quantum algorithms for TDA offer only a polynomial advantage. We verify our claim by showing that the seminal quantum algorithm for TDA developed by Lloyd, Garnerone and Zanardi [1] achieves a quadratic speedup over the best classical approach on average, and a power-of-four speedup in the best case. Finally, we argue that an exponential quantum advantage can be recovered if the data is given as a specification of simplices rather than as a list of vertices and edges -for example, if we wish to calculate the homology of Facebook from a list of Facebook groups and their members rather than from a list of pairwise interactions between Facebook users.