# Quantum Ensemble Classification: A Sampling-based Learning Control Approach

arXiv: https://arxiv.org/abs/1312.6945 · HTML: https://mlsanity.com/paper/1312.6945/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2013.

## Built on by (most cited first, 2 of 2)

- [Tomography of Quantum States from Structured Measurements via quantum-aware transformer](https://mlsanity.com/paper/2305.05433/) (2023)
- [QiNN-QJ: A Quantum-inspired Neural Network with Quantum Jump for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2510.27091/) (2025)

## Abstract (arXiv)

Quantum ensemble classification has significant applications in discrimination of atoms (or molecules), separation of isotopic molecules and quantum information extraction. However, quantum mechanics forbids deterministic discrimination among nonorthogonal states. The classification of inhomogeneous quantum ensembles is very challenging since there exist variations in the parameters characterizing the members within different classes. In this paper, we recast quantum ensemble classification as a supervised quantum learning problem. A systematic classification methodology is presented by using a sampling-based learning control (SLC) approach for quantum discrimination. The classification task is accomplished via simultaneously steering members belonging to different classes to their corresponding target states (e.g., mutually orthogonal states). Firstly a new discrimination method is proposed for two similar quantum systems. Then an SLC method is presented for quantum ensemble classification. Numerical results demonstrate the effectiveness of the proposed approach for the binary classification of two-level quantum ensembles and the multiclass classification of multilevel quantum ensembles.