2 citations · 2 across the 3 of their papers we have counts for
5 papers
Comment on "A General Framework for Constructing Local Hidden-state Models to Determine the Steerability"
Nick von Selzam, Florian Marquardt
We point out that the method presented in a recent arXiv article by Jia et al. (arXiv:2512.21848) for constructing local hidden-state models closely follows the framework we develo…
Reinforcement Learning for Quantum Technology
Marin Bukov, Florian Marquardt
Many challenges arising in Quantum Technology can be successfully addressed using a set of machine learning algorithms collectively known as reinforcement learning (RL), based on a…
Quantum computing and artificial intelligence: status and perspectives
Giovanni Acampora, Andris Ambainis, Natalia Ares +36
This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could supp…
Meta-learning characteristics and dynamics of quantum systems
Lucas Schorling, Pranav Vaidhyanathan, Jonas Schuff +7
While machine learning holds great promise for quantum technologies, most current methods focus on predicting or controlling a specific quantum system. Meta-learning approaches, ho…
Quantum feedback control with a transformer neural network architecture
Pranav Vaidhyanathan, Florian Marquardt, Mark T. Mitchison +1
Attention-based neural networks such as transformers have revolutionized various fields such as natural language processing, genomics, and vision. Here, we demonstrate the use of t…