Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

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 …

Variational quantum algorithms are ubiquitous in applications of noisy intermediate-scale
quantum computers. Due to the structure of conventional parametrized quantum gates, the …

Recent advances in classical reinforcement learning (RL) and quantum computation point to
a promising direction for performing RL on a quantum computer. However, potential …

To harness the potential of noisy intermediate-scale quantum devices, it is paramount to find
the best type of circuits to run hybrid quantum-classical algorithms. Key candidates are …

We develop quantum protocols for anomaly detection and apply them to the task of credit
card fraud detection (FD). First, we establish classical benchmarks based on supervised and …

Exploiting near-term quantum computers and achieving practical value is a considerable
and exciting challenge. Most prominent candidates as variational algorithms typically aim to …

Optimization of unitary transformations in variational quantum algorithms benefits highly
from efficient evaluation of cost function gradients with respect to amplitudes of unitary …

We propose several approaches for solving regression problems and differential equations
(DEs) with quantum kernel methods. We compose quantum models as weighted sums of …