# Guided Quantum Compression for High Dimensional Data Classification

arXiv: https://arxiv.org/abs/2402.09524 · HTML: https://mlsanity.com/paper/2402.09524/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 70 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Generalization in quantum machine learning from few training data](https://mlsanity.com/paper/2111.05292/) (2021)
- [Supervised quantum machine learning models are kernel methods](https://mlsanity.com/paper/2101.11020/) (2021)
- [Quantum advantage in learning from experiments](https://mlsanity.com/paper/2112.00778/) (2021)
- [Quantum machine learning beyond kernel methods](https://mlsanity.com/paper/2110.13162/) (2021)
- [The Inductive Bias of Quantum Kernels](https://mlsanity.com/paper/2106.03747/) (2021)
- [Machine Learning in the Search for New Fundamental Physics](https://mlsanity.com/paper/2112.03769/) (2021)
- [Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spaces](https://mlsanity.com/paper/2009.00298/) (2020)
- [Machine Learning for Anomaly Detection in Particle Physics](https://mlsanity.com/paper/2312.14190/) (2023)
- [Style-based quantum generative adversarial networks for Monte Carlo events](https://mlsanity.com/paper/2110.06933/) (2021)
- [A Brief Review of Deep Multi-task Learning and Auxiliary Task Learning](https://mlsanity.com/paper/2007.01126/) (2020)
- [End-to-end Sinkhorn Autoencoder with Noise Generator](https://mlsanity.com/paper/2006.06704/) (2020)
- [On establishing learning separations between classical and quantum machine learning with classical data](https://mlsanity.com/paper/2208.06339/) (2022)
- [Higgs analysis with quantum classifiers](https://mlsanity.com/paper/2104.07692/) (2021)
- [Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics](https://mlsanity.com/paper/2103.15470/) (2021)
- [A super-polynomial quantum-classical separation for density modelling](https://mlsanity.com/paper/2210.14936/) (2022)

## Built on by (most cited first, 3 of 3)

- [Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning](https://mlsanity.com/paper/2508.00024/) (2025)
- [Guided Graph Compression for Quantum Graph Neural Networks](https://mlsanity.com/paper/2506.09862/) (2025)
- [Medical Imaging Classification with Cold-Atom Reservoir Computing using Auto-Encoders and Surrogate-Driven Training](https://mlsanity.com/paper/2605.06727/) (2026)

## 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.