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Variational Quantum Algorithms for Semidefinite Programming
Dhrumil Patel, Patrick J. Coles, Mark M. Wilde
A semidefinite program (SDP) is a particular kind of convex optimization problem with applications in operations research, combinatorial optimization, quantum information science,…
Generalization in quantum machine learning from few training data
Matthias C. Caro, Hsin-Yuan Huang, M. Cerezo +4
Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a test…
Subtleties in the trainability of quantum machine learning models
Supanut Thanasilp, Samson Wang, Nhat A. Nghiem +2
A new paradigm for data science has emerged, with quantum data, quantum models, and quantum computational devices. This field, called Quantum Machine Learning (QML), aims to achiev…
Theory of overparametrization in quantum neural networks
Martin Larocca, Nathan Ju, Diego García-Martín +2
The prospect of achieving quantum advantage with Quantum Neural Networks (QNNs) is exciting. Understanding how QNN properties (e.g., the number of parameters ) affect the loss l…
Entangled Datasets for Quantum Machine Learning
Louis Schatzki, Andrew Arrasmith, Patrick J. Coles +1
High-quality, large-scale datasets have played a crucial role in the development and success of classical machine learning. Quantum Machine Learning (QML) is a new field that aims…
Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
Samson Wang, Piotr Czarnik, Andrew Arrasmith +3
Variational Quantum Algorithms (VQAs) are often viewed as the best hope for near-term quantum advantage. However, recent studies have shown that noise can severely limit the traina…