Quantum diffusion map for nonlinear dimensionality reduction

TL;DR

This paper introduces a quantum algorithm for diffusion maps that significantly reduces computational complexity, enabling efficient nonlinear dimensionality reduction and quantum phase classification in large datasets.

Contribution

The paper presents a quantum diffusion map (qDM) algorithm that performs eigen-decomposition in logarithmic time, offering a quantum speedup over classical methods for nonlinear dimensionality reduction.

Findings

Quantum eigen-decomposition achieved in O(log^3 N) time

Total runtime scales as N^2 polylog N for constructing diffusion maps

Quantum subroutines useful for other random walk algorithms

Abstract

Inspired by random walk on graphs, diffusion map (DM) is a class of unsupervised machine learning that offers automatic identification of low-dimensional data structure hidden in a high-dimensional dataset. In recent years, among its many applications, DM has been successfully applied to discover relevant order parameters in many-body systems, enabling automatic classification of quantum phases of matter. However, classical DM algorithm is computationally prohibitive for a large dataset, and any reduction of the time complexity would be desirable. With a quantum computational speedup in mind, we propose a quantum algorithm for DM, termed quantum diffusion map (qDM). Our qDM takes as an input $N$ classical data vectors, performs an eigen-decomposition of the Markov transition matrix in time $O(\log^3 N)$, and classically constructs the diffusion map via the readout (tomography) of the eigenvectors, giving a total expected runtime proportional to $N^2 \text{polylog}\, N$. Lastly, quantum subroutines in qDM for constructing a Markov transition matrix, and for analyzing its spectral properties can also be useful for other random walk-based algorithms.