806 citations · 5.4k across the 44 of their papers we have counts for
9 papers · 2 filters
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…
Unifying and benchmarking state-of-the-art quantum error mitigation techniques
Daniel Bultrini, Max Hunter Gordon, Piotr Czarnik +4
Error mitigation is an essential component of achieving a practical quantum advantage in the near term, and a number of different approaches have been proposed. In this work, we re…
Equivalence of quantum barren plateaus to cost concentration and narrow gorges
Andrew Arrasmith, Zoë Holmes, M. Cerezo +1
Optimizing parameterized quantum circuits (PQCs) is the leading approach to make use of near-term quantum computers. However, very little is known about the cost function landscape…
A semi-agnostic ansatz with variable structure for quantum machine learning
M. Bilkis, M. Cerezo, Guillaume Verdon +2
Quantum machine learning -- and specifically Variational Quantum Algorithms (VQAs) -- offers a powerful, flexible paradigm for programming near-term quantum computers, with applica…
Connecting ansatz expressibility to gradient magnitudes and barren plateaus
Zoë Holmes, Kunal Sharma, M. Cerezo +1
Parameterized quantum circuits serve as ansätze for solving variational problems and provide a flexible paradigm for programming near-term quantum computers. Ideally, such ansätze…