Exponential quantum speedup in simulating coupled classical oscillators

Babbush, Ryan; Berry, Dominic W.; Kothari, Robin; Somma, Rolando D.; Wiebe, Nathan

doi:10.48550/arxiv.2303.13012

Cited by 1 publication

(1 citation statement)

References 41 publications

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“…Machine Learning (ML) is established as an invaluable tool for analysing data and assisting many physics analyses at the Large Hadron Collider (LHC) [1][2][3][4]. Meanwhile, quantum computing is a fundamentally different paradigm for information processing, that is known to provide computational speed-ups over classical methods for a large class of problems [5][6][7][8][9][10][11]. Furthermore, Quantum Machine Learning (QML) has the potential to enhance traditional ML methods [12][13][14][15][16][17] and yields various advantages in specific learning tasks [18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Guided quantum compression for high dimensional data classification

Belis,

Odagiu,

Grossi

et al. 2024

Mach. Learn.: Sci. Technol.

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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.

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