Guided Quantum Compression for Higgs Identification

arXiv:2402.09524v1 Announce Type: cross
Abstract: Quantum machine learning provides a fundamentally novel and promising approach to analyzing data. However, many data sets are too complex for currently available quantum computers. Consequently, quantum machine learning applications conventionally resort to dimensionality reduction algorithms, e.g., auto-encoders, before passing data through the quantum models. We show that using a classical auto-encoder as an independent preprocessing step can significantly decrease the classification performance of a quantum machine learning algorithm. To ameliorate this issue, we design …

abstract algorithms applications arxiv auto compression computers cs.lg data data sets dimensionality hep-ex identification machine machine learning machine learning applications novel quant-ph quantum quantum computers show through type