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Challenges and opportunities in quantum machine learning
At the intersection of machine learning and quantum computing, quantum machine learning
has the potential of accelerating data analysis, especially for quantum data, with …
has the potential of accelerating data analysis, especially for quantum data, with …
A survey of important issues in quantum computing and communications
Driven by the rapid progress in quantum hardware, recent years have witnessed a furious
race for quantum technologies in both academia and industry. Universal quantum …
race for quantum technologies in both academia and industry. Universal quantum …
Quantum computing for high-energy physics: State of the art and challenges
Quantum computers offer an intriguing path for a paradigmatic change of computing in the
natural sciences and beyond, with the potential for achieving a so-called quantum …
natural sciences and beyond, with the potential for achieving a so-called quantum …
Generalization in quantum machine learning from few training data
Modern quantum machine learning (QML) methods involve variationally optimizing a
parameterized quantum circuit on a training data set, and subsequently making predictions …
parameterized quantum circuit on a training data set, and subsequently making predictions …
Is quantum advantage the right goal for quantum machine learning?
Machine learning is frequently listed among the most promising applications for quantum
computing. This is in fact a curious choice: the machine-learning algorithms of today are …
computing. This is in fact a curious choice: the machine-learning algorithms of today are …
Group-invariant quantum machine learning
Quantum machine learning (QML) models are aimed at learning from data encoded in
quantum states. Recently, it has been shown that models with little to no inductive biases (ie …
quantum states. Recently, it has been shown that models with little to no inductive biases (ie …
Better than classical? the subtle art of benchmarking quantum machine learning models
Benchmarking models via classical simulations is one of the main ways to judge ideas in
quantum machine learning before noise-free hardware is available. However, the huge …
quantum machine learning before noise-free hardware is available. However, the huge …
Theory of overparametrization in quantum neural networks
The prospect of achieving quantum advantage with quantum neural networks (QNNs) is
exciting. Understanding how QNN properties (for example, the number of parameters M) …
exciting. Understanding how QNN properties (for example, the number of parameters M) …
Theory for equivariant quantum neural networks
Quantum neural network architectures that have little to no inductive biases are known to
face trainability and generalization issues. Inspired by a similar problem, recent …
face trainability and generalization issues. Inspired by a similar problem, recent …
Representation theory for geometric quantum machine learning
Recent advances in classical machine learning have shown that creating models with
inductive biases encoding the symmetries of a problem can greatly improve performance …
inductive biases encoding the symmetries of a problem can greatly improve performance …