| 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
- Data Encoding: Map input data points into quantum input states of the network.
- Transport Dynamics: Evolve the network under the GKSL master equation to steady state.
- Readout: Extract cluster assignments from terminal output currents (no tomography needed).
- 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
