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

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 …

While quantum state tomography is notoriously hard, most states hold little interest to
practically minded tomographers. Given that states and unitaries appearing in nature are of …

In this paper, we provide an overview of the recent work in the quantum finance realm from
various perspectives. The applications in consideration are Portfolio Optimization, Fraud …

Quantum machine learning is the field that aims to integrate machine learning with quantum
computation. In recent years, the field has emerged as an active research area with the …

We propose and assess an alternative quantum generator architecture in the context of
generative adversarial learning for Monte Carlo event generation, used to simulate particle …

We introduce a versatile method for preparing a quantum state whose amplitudes are given
by some known function. Unlike existing approaches, our method does not require …

Quantum neural networks (QNNs) have emerged as a leading strategy to establish
applications in machine learning, chemistry, and optimization. While the applications of QNN …

Machine learning techniques are essential tools to compute efficient, yet accurate, force
fields for atomistic simulations. This approach has recently been extended to incorporate …

Universal approximation theorems are the foundations of classical neural networks,
providing theoretical guarantees that the latter are able to approximate maps of interest …