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Guided quantum compression for high dimensional data classification
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DOI：10.1088/2632-2153/ad5fdd.png)
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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.
Keyword:
quantum machine learning
deep learning
particle physics
feature extraction
quantum computing
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4.6
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1.1K
被引数:
3.4K
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分享收藏Application of quantum machine learning using the quantum variational classifier method to high energy physics analysis at the LHC on IBM quantum computer simulator and hardware with 10 qubits使用量子变分分类器方法的量子机器学习在IBM量子计算机模拟器和具有10个量子位的硬件上的LHC上的高能物理分析中的应用OAAI
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