This is a package developed in the Mesoscience Lab to perform Hierarchy of Pure State (HOPS) calculations using an adaptive basis that provides size-invariant scaling for large systems.
-
Updated
May 11, 2026 - Python
#
quantum-state-diffusion
Here are 3 public repositories matching this topic...
Simulation of the quantum trajectories of Schrödinger cat states
wiener-process open-quantum-systems stochastic-differential-equation lindblad-master-equation quantum-state-diffusion master-equation-unraveling poisson-unraveling wiener-unraveling
-
Updated
May 22, 2025 - Jupyter Notebook
Implement hierarchical deep classification using coarse-to-fine learning to improve accuracy on datasets with labeled taxonomies like CIFAR-100.
twitter text-classification tensorflow salesforce bypassing statistical-machine-translation multi-label phrase-based-translation multilabel multi-layer-cache hierarchical-multilabel ujaran-kebencian ujaran exciton-energy-transfer quantum-state-diffusion salesforce-trailblazers
-
Updated
Jul 27, 2026 - Python
Improve this page
Add a description, image, and links to the quantum-state-diffusion topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the quantum-state-diffusion topic, visit your repo's landing page and select "manage topics."