Accelerating Spectral Clustering on Quantum and Analog Platforms

TL;DR

This paper presents a hybrid quantum-analog algorithm that accelerates spectral clustering by reducing complexity from cubic to linear time, enabling efficient large-scale graph clustering through quantum evolution and analog computation.

Contribution

It introduces a novel hybrid quantum-analog method that significantly speeds up spectral clustering by avoiding eigendecomposition, leveraging quantum dynamics and analog processing.

Findings

Reduces spectral clustering complexity from O(N^3) to O(N).

Uses quantum evolution and analog DMD for eigenvalue extraction.

Demonstrates practical large-scale graph clustering acceleration.

Abstract

We introduce a novel hybrid quantum-analog algorithm to perform graph clustering that exploits connections between the evolution of dynamical systems on graphs and the underlying graph spectra. This approach constitutes a new class of algorithms that combine emerging quantum and analog platforms to accelerate computations. Our hybrid algorithm is equivalent to spectral clustering and significantly reduces the computational complexity from $O(N^3)$ to $O(N)$, where $N$ is the number of nodes in the graph. We achieve this speedup by circumventing the need for explicit eigendecomposition of the normalized graph Laplacian matrix, which dominates the classical complexity, and instead leveraging quantum evolution of the Schr\"{o}dinger equation followed by efficient analog computation for the dynamic mode decomposition (DMD) step. Specifically, while classical spectral clustering requires $O(N^3)$ operations to perform eigendecomposition, our method exploits the natural quantum evolution of states according to the graph Laplacian Hamiltonian in linear time, combined with the linear scaling for DMD that leverages efficient matrix-vector multiplications on analog hardware. We prove and demonstrate that this hybrid approach can extract the eigenvalues and scaled eigenvectors of the normalized graph Laplacian by evolving Schr\"{o}dinger dynamics on quantum computers followed by DMD computations on analog devices, providing a significant computational advantage for large-scale graph clustering problems. Our demonstrations can be reproduced using our code that has been released at https://github.com/XingziXu/quantum-analog-clustering.