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

Growing interest in quantum computing for practical applications has led to a surge in the
availability of programmable machines for executing quantum algorithms,. Present-day …

Abstract The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising
variational quantum algorithm that aims to solve combinatorial optimization problems that …

It is unknown whether near-term quantum computers are advantageous for machine
learning tasks. In this work we address this question by trying to understand how powerful …

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 …

Quantum computers can be used for supervised learning by treating parametrized quantum
circuits as models that map data inputs to predictions. While a lot of work has been done to …

Quantum neural networks (QNNs) have generated excitement around the possibility of
efficiently analyzing quantum data. But this excitement has been tempered by the existence …

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

PennyLane is a Python 3 software framework for differentiable programming of quantum
computers. The library provides a unified architecture for near-term quantum computing …