Qlustering for Data Clustering via Network-Based Quantum Transport

TL;DR

Qlustering introduces a quantum network-based clustering method that uses steady-state transport observables for unsupervised learning, avoiding complex state tomography and enabling practical quantum data analysis.

Contribution

It presents a novel hybrid classical-quantum clustering framework leveraging quantum transport in networks, with algorithm-hardware co-design and broad applicability.

Findings

Competitive performance on synthetic and real datasets.

Stable clustering results across various dephasing strengths.

Transport observables effectively reveal data structure without full state tomography.

Abstract

Analog quantum computation offers a route to machine learning using controllable physical dynamics as a computational resource. However, many existing approaches rely on task-specific protocols or observables that are difficult to access experimentally, limiting generality and implementation. Here we introduce Qlustering, an unsupervised clustering framework based on steady-state quantum transport in quantum networks governed by the GKSL master equation, developed through algorithm-hardware co-design. Data are encoded as input states, and cluster assignments are inferred from steady-state output currents, avoiding full state tomography in favor of accessible transport observables. The method realizes a hybrid classical-quantum workflow in which data preparation and training are performed classically, while clustering is carried out by transport dynamics. We benchmark the method on synthetic datasets, localization, and QM9 and Iris, finding competitive performance and stability over a broad range of dephasing strengths. These results show that unlabeled data structure can be extracted directly from steady-state transport observables, identifying terminal-current readout as a native, tomography-free mechanism for unsupervised learning in open quantum networks.