Guided Quantum Compression for High Dimensional Data Classification
This work addresses the problem of handling high-dimensional data in quantum computing for researchers in quantum machine learning, offering an incremental improvement by integrating classical and quantum components.
The paper tackles the challenge of applying quantum machine learning to complex datasets by introducing a guided quantum compression model that unifies dimensionality reduction and quantum classification, achieving better performance than conventional quantum approaches and deep learning benchmarks on Higgs boson identification.
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.