Guided Quantum Compression for High Dimensional Data Classification
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866913605089230848 |
|---|---|
| author | Belis, Vasilis Odagiu, Patrick Grossi, Michele Reiter, Florentin Dissertori, Günther Vallecorsa, Sofia |
| author_facet | Belis, Vasilis Odagiu, Patrick Grossi, Michele Reiter, Florentin Dissertori, Günther Vallecorsa, Sofia |
| contents | Quantum machine learning provides a fundamentally different approach to analyzing data. However, many interesting datasets are too complex for currently available quantum computers. Present quantum machine learning applications usually diminish this complexity by reducing the dimensionality of the data, e.g., via auto-encoders, before passing it through the quantum models. Here, we design a classical-quantum paradigm that unifies the dimensionality reduction task with a quantum classification model into a single architecture: the guided quantum compression model. We exemplify how this architecture outperforms conventional quantum machine learning approaches on a challenging binary classification problem: identifying the Higgs boson in proton-proton collisions at the LHC. Furthermore, the guided quantum compression model shows better performance compared to the deep learning benchmark when using solely the kinematic variables in our dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09524 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Guided Quantum Compression for High Dimensional Data Classification Belis, Vasilis Odagiu, Patrick Grossi, Michele Reiter, Florentin Dissertori, Günther Vallecorsa, Sofia Quantum Physics Machine Learning High Energy Physics - Experiment Quantum machine learning provides a fundamentally different approach to analyzing data. However, many interesting datasets are too complex for currently available quantum computers. Present quantum machine learning applications usually diminish this complexity by reducing the dimensionality of the data, e.g., via auto-encoders, before passing it through the quantum models. Here, we design a classical-quantum paradigm that unifies the dimensionality reduction task with a quantum classification model into a single architecture: the guided quantum compression model. We exemplify how this architecture outperforms conventional quantum machine learning approaches on a challenging binary classification problem: identifying the Higgs boson in proton-proton collisions at the LHC. Furthermore, the guided quantum compression model shows better performance compared to the deep learning benchmark when using solely the kinematic variables in our dataset. |
| title | Guided Quantum Compression for High Dimensional Data Classification |
| topic | Quantum Physics Machine Learning High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2402.09524 |