Challenges and opportunities in quantum machine learning

By M. Cerezo, Guillaume Verdon, Hsin-Yuan Huang, Lukasz Cincio, Patrick J. Coles. Nature Computational Science, 2022

804 citations recorded by the source corpus.

At the intersection of machine learning and quantum computing, quantum machine learning has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry and high-energy physics. Nevertheless, challenges remain regarding the trainability of quantum machine learning models. Here we review current methods and applications for quantum machine learning. We highlight differences between quantum and classical machine learning, with a focus on quantum neural networks and quantum deep learning. Finally, we discuss opportunities for quantum advantage with quantum machine learning. Quantum machine learning has become an essential tool to process and analyze the increased amount of quantum data. Despite recent progress, there are still many challenges to be addressed and myriad future avenues of research.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/ab06951251e0abfdb866694f9a23a79c72784317

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In Artificial Intelligence

In Atomic and Molecular Physics, and Optics

Citations in the corpus

Barren plateaus in variational quantum computing (2024, positive): “One can explicitly verify this by using the Baker–Campbell–Hausdorff formula to re-express the composition of layers as the exponential of a linear combination of the nested commutators of the generators, which by definition belongs in g .”