At the intersection of machine learning and quantum computing, quantum machine learning
has the potential of accelerating data analysis, especially for quantum data, with …

Quantum computers have demonstrable ability to solve problems at a scale beyond brute-
force classical simulation. Interest in quantum algorithms has developed in many areas …

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 …

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 …

Abstract Machine learning algorithms based on parametrized quantum circuits are prime
candidates for near-term applications on noisy quantum computers. In this direction, various …

The trend of today's electronic developments is the reduction of essential devices and the
expansion of their functionality. This creates a demand for new nano-elements that can …

Despite the great promise of quantum machine learning models, there are several
challenges one must overcome before unlocking their full potential. For instance, models …

Quantum machine learning is often highlighted as one of the most promising practical
applications for which quantum computers could provide a computational advantage …

With the advent of real-world quantum computing, the idea that parametrized quantum
computations can be used as hypothesis families in a quantum-classical machine learning …