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 …

This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel
discipline that brings together concepts from Machine Learning (ML), Quantum Computing …

Variational quantum eigensolver (VQE) emerged as a first practical algorithm for near-term
quantum computers. Its success largely relies on the chosen variational ansatz …

Quantum machine learning has emerged as an exciting and promising paradigm inside
quantum technologies. It may permit, on the one hand, to carry out more efficient machine …

Very often when studying non-equilibrium systems one is interested in analysing dynamical
behaviour that occurs with very low probability, so called rare events. In practice, since rare …

Machine learning techniques provide a remarkable tool for advancing scientific research,
and this area has significantly grown in the past few years. In particular, reinforcement …

Quantum machine learning (QML) is a discipline that holds the promise of revolutionizing
data processing and problem‐solving. However, dissipation and noise arising from the …

We demonstrate that deep reinforcement learning (deep RL) provides a highly effective
strategy for the control and self-tuning of optical systems. Deep RL integrates the two …

Quantum machine learning has emerged as a promising paradigm that could accelerate
machine learning calculations. Inside this field, quantum reinforcement learning aims at …

Due to the remarkable progress of ever-growing digitalisation and computing capabilities,
data has become increasingly abundant, and machine learning has emerged as a key …