Quantum computers are expected to surpass the computational capabilities of classical
computers and have a transformative impact on numerous industry sectors. We present a …

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 …

One of the most important properties of classical neural networks is how surprisingly
trainable they are, though their training algorithms typically rely on optimizing complicated …

A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …

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 …

Despite its undeniable success, classical machine learning remains a resource-intensive
process. Practical computational efforts for training state-of-the-art models can now only be …

The rapid development of artificial intelligence (AI) demands the rapid development of
domain-specific hardware specifically designed for AI applications. Neuro-inspired …

Variational quantum computing offers a flexible computational paradigm with applications in
diverse areas. However, a key obstacle to realizing their potential is the Barren Plateau (BP) …

We argue that an excess in entanglement between the visible and hidden units in a
quantum neural network can hinder learning. In particular, we show that quantum neural …

Neural networks enjoy widespread success in both research and industry and, with the
advent of quantum technology, it is a crucial challenge to design quantum neural networks …