arXiv subjects

Maciej Wiśniewski

Publications and source records attributed to Maciej Wiśniewski.

3 recordsLinked to original sources

arXiv2026

Optical downlink modeling for low-earth-orbit and medium-earth-orbit satellites under atmospheric turbulence with a quantum-state-tomography use case

This paper presents a comprehensive analysis of the link budget for free-space optical systems involving Low Earth Orbit (LEO) and Medium Earth Orbit (MEO) satellites. We develop a detailed model of the satellite-to-ground channel that accounts for the primary physical processes affecting transmittance: atmospheric absorption and scattering, free-space diffraction, and turbulence-induced fluctuations. The study introduces a general method for computing transmittance along a slant path between a satellite and an optical ground station, incorporating zenith angle, slant range, and altitude-dependent attenuation. The proposed framework is intended to support the design and evaluation of space-based optical links and serves as a critical tool for defining technical specifications in satellite communication demonstrators and simulations. Numerical estimates are provided to illustrate the magnitude of losses under typical operational conditions, including the role of aperture averaging. In addition to the link budget analysis, we introduce a satellite-based quantum use case. We propose a scheme for quantum state tomography performed on states generated by an onboard photon source on an LEO or MEO satellite and transmitted to the optical ground station. This approach enables continuous verification of the quality of quantum resources that can be used to perform quantum protocols within quantum information networks.

quant-ph↗

arXiv2026

Machine Learning of Quantum Entanglement from Noisy Measurements

In this work, we investigate the application of Machine Learning (ML) algorithms to the identification and quantitative characterization of quantum entanglement in polarization-entangled photon pairs. The analysis is based on simulated symmetric, informationally complete, positive operator-valued measure (SIC-POVM) measurement data, where each two-qubit state is represented by a 16-dimensional measurement vector corresponding to experimentally accessible coincidence counts. The generated SIC-POVM measurement data include Poissonian shot noise. Several supervised ML algorithms, including Logistic Regression, k-Nearest Neighbors, Decision Trees, Support Vector Machines, and Random Forests, are applied to the classification of separable and entangled states directly from raw measurement data, without explicit density matrix reconstruction or the use of conventional separability criteria. The study additionally explores clustering methods and nonlinear regression techniques for estimating continuous entanglement measures. The obtained results demonstrate that ML methods can achieve very high classification accuracy, even under extremely limited training conditions. These findings indicate that ML may provide an efficient alternative to conventional quantum-state analysis under simulated Poissonian noise conditions.

quant-ph↗

arXiv2024

RapidDock: Unlocking Proteome-scale Molecular Docking

Accelerating molecular docking -- the process of predicting how molecules bind to protein targets -- could boost small-molecule drug discovery and revolutionize medicine. Unfortunately, current molecular docking tools are too slow to screen potential drugs against all relevant proteins, which often results in missed drug candidates or unexpected side effects occurring in clinical trials. To address this gap, we introduce RapidDock, an efficient transformer-based model for blind molecular docking. RapidDock achieves at least a $100 \times$ speed advantage over existing methods without compromising accuracy. On the Posebusters and DockGen benchmarks, our method achieves $52.1\%$ and $44.0\%$ success rates ($\text{RMSD}<2$Å), respectively. The average inference time is $0.04$ seconds on a single GPU, highlighting RapidDock's potential for large-scale docking studies. We examine the key features of RapidDock that enable leveraging the transformer architecture for molecular docking, including the use of relative distance embeddings of $3$D structures in attention matrices, pre-training on protein folding, and a custom loss function invariant to molecular symmetries.

q-bio.BM↗