Abstract Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …

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 …

Abstract Machine learning has achieved dramatic success over the past decade, with
applications ranging from face recognition to natural language processing. Meanwhile, rapid …

Quantum machine learning models have shown successful generalization performance
even when trained with few data. In this work, through systematic randomization …

The superiority of variational quantum algorithms (VQAs) such as quantum neural networks
(QNNs) and variational quantum eigensolvers (VQEs) heavily depends on the expressivity …

Quantum neural networks (QNNs) have become an important tool for understanding the
physical world, but their advantages and limitations are not fully understood. Some QNNs …

The expressive capacity of physical systems employed for learning is limited by the
unavoidable presence of noise in their extracted outputs. Though present in physical …

Quantum chaos has become a cornerstone of physics through its many applications. One
trademark of quantum chaotic systems is the spread of local quantum information, which …

Quantum tangent kernel methods provide an efficient approach to analyzing the
performance of quantum machine learning models in the infinite-width limit, which is of …

As a hybrid of artificial intelligence and quantum computing, quantum neural networks
(QNNs) have gained significant attention as a promising application on near-term, noisy …