Harmoniq: Efficient Data Augmentation on a Quantum Computer Inspired by Harmonic Analysis
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866913048830148608 |
|---|---|
| author | Kirova, Kristina Doerfler, Monika Luef, Franz Kueng, Richard |
| author_facet | Kirova, Kristina Doerfler, Monika Luef, Franz Kueng, Richard |
| contents | Quantum machine learning has attracted significant interest in recent years. Most existing approaches, however, are variational in nature and require extensive parameter optimization subroutines. Here, we propose a conceptually distinct quantum machine learning approach that goes beyond the variational paradigm. Harmoniq takes a novel data augmentation technique from quantum harmonic analysis and approximates it as a stochastic mixture of n-qubit circuits with (at most) quadratic depth each. A key strength of Harmoniq is its modularity: viewed as a quantum process acting on density matrices, it can readily be combined with other quantum data processing and learning subroutines. A subsequent case study demonstrates this modularity by combining Harmoniq with stochastic amplitude encoding for the input density matrix and quantum PCA on the output density matrix. This results in a promising signal denoising pipeline that works particularly well in the small sample size regime. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18691 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Harmoniq: Efficient Data Augmentation on a Quantum Computer Inspired by Harmonic Analysis Kirova, Kristina Doerfler, Monika Luef, Franz Kueng, Richard Quantum Physics Mathematical Physics Quantum machine learning has attracted significant interest in recent years. Most existing approaches, however, are variational in nature and require extensive parameter optimization subroutines. Here, we propose a conceptually distinct quantum machine learning approach that goes beyond the variational paradigm. Harmoniq takes a novel data augmentation technique from quantum harmonic analysis and approximates it as a stochastic mixture of n-qubit circuits with (at most) quadratic depth each. A key strength of Harmoniq is its modularity: viewed as a quantum process acting on density matrices, it can readily be combined with other quantum data processing and learning subroutines. A subsequent case study demonstrates this modularity by combining Harmoniq with stochastic amplitude encoding for the input density matrix and quantum PCA on the output density matrix. This results in a promising signal denoising pipeline that works particularly well in the small sample size regime. |
| title | Harmoniq: Efficient Data Augmentation on a Quantum Computer Inspired by Harmonic Analysis |
| topic | Quantum Physics Mathematical Physics |
| url | https://arxiv.org/abs/2604.18691 |