Guided Quantum Compression for High Dimensional Data Classification
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 23 of its 70 references that are in the map. First posted on arXiv 14 Feb 2024.
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23 of 70 references.
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11 more
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.