To communicate and exchange resources
Super-resolution imaging has transformed scientific disciplines, from molecular biology to astronomy, by enabling visualization beyond the diffraction limit. However, current super-resolution methods require extensive calibration for each imaging setup, restricting their applicability. Our latest research introduces a novel AI-driven, device-agnostic
We are thrilled to share our research on the quantification of quantum entanglement using artificial neural networks. Our approach allows us to accurately measure entanglement without requiring complete knowledge of the quantum state. By leveraging neural networks, we achieve a
Quantum processors and sensors promise to outperform their classical counterparts. Still, they require classical control signals, which affect their operation in a highly nontrivial way. An example: electrical voltages controlling a photonic chip or a superconducting circuit. We developed a
![[MLQ2021 logo]](https://quantum.opticsolomouc.org/wp-content/uploads/2021/02/MLQ2021_logo-600x350.png "QOLO presentations at the Machine Learning for Quantum 2021"){kind=logo}
Dominik Koutný and Dominik Vašinka from our group will present video posters at the MLQ2021. The goal of this online conference, taking place March 1-5, 2021, is to bring together quantum physicists with experts in computer science and machine learning,