qlustering-quantum-clustering

name

qlustering-quantum-clustering

description

Unsupervised clustering via steady-state quantum transport in open quantum networks (GKSL master equation). Encodes data as input states and infers cluster assignments from terminal current observables - no full state tomography required. Use when: quantum clustering, GKSL transport, analog quantum ML, open quantum network clustering, Qlustering algorithm, steady-state quantum transport clustering, tomography-free quantum learning, quantum unsupervised learning, algorithm-hardware co-design clustering. Triggered by: Qlustering, quantum transport clustering, GKSL master equation clustering, analog quantum computation clustering, steady-state quantum current clustering, quantum network unsupervised learning.

Qlustering: Quantum Transport-Based Clustering

Unsupervised clustering framework using steady-state quantum transport in open quantum networks. Data are encoded as input states; cluster assignments are inferred from steady-state output currents measured at terminals.

Paper

arXiv: 2605.10844v1 — Qlustering for Data Clustering via Network-Based Quantum Transport by Shmuel Lorber, Yonatan Dubi (May 2026).

Core Approach

1. Data Encoding: Map input data points into quantum input states of the network.
2. Transport Dynamics: Evolve the network under the GKSL master equation to steady state.
3. Readout: Extract cluster assignments from terminal output currents (no tomography needed).
4. Training-free: Classical data preparation; clustering carried out purely by transport dynamics.

When to Use

• Unsupervised clustering tasks where classical methods struggle
• Quantum machine learning implementations on analog quantum hardware
• Scenarios requiring noise-robust clustering (stable across broad dephasing strengths)
• Algorithm-hardware co-design for near-term quantum devices

Key Properties

• No tomography: Uses accessible transport observables (terminal currents)
• Noise-robust: Stable performance across wide range of dephasing strengths
• Hybrid workflow: Classical data prep + quantum transport clustering
• Benchmarked: Tested on synthetic datasets, QM9, Iris