New Features Processing Nodes: Networks can now have an arbitrary numbers of node layers. The first layer contains PrepareNodes, the final layer contains MeasureNodes, and intermediate layers contain either NoiseNodes or ProcessingNodes. Cost functions were updated to handle arbitrary numbers of layers. PennyLane is updated to current version 0.27. Breaking Changes for the NetworkAnsatz constructor, the positional argument noise_nodes can no longer go behind the measurement node layer. All nodes, must be passed to NetworkAnsatz as positional arguments in the appropriate ordering. The last set of nodes must be a measurement layer, however, the remaining layers are generic. Networks with noise nodes no longer use "default.mixed" automatica...
The main improvement in version 2.1 is that now support the Keras functional API in the Graph and Li...
Updated network generation which is only compatible with Mathematica v13+. Random batch training wi...
With the 2.0 release, NNGT moves from inheritance to composition for the underlying graphs. This ena...
New Features Processing Nodes: Networks can now have an arbitrary numbers of node layers. The first...
In this release we are pleased to announce that local operations and classical communication (LOCC) ...
Breaking Changes Network settings are now stored in a 1D list rather than the nested ragged array s...
Features: shannon_entropy_cost_fn : optimize shannon entropy in network added __version__ attribute...
In this release we add a few new utilities and ansatzes. state_vec_fn : constructs a function that ...
comment: the increment to `0.3.` is not due to a new feature*. For now on we increment the second nu...
The Queueing Network Analysis Tool (QNAT) is a powerful package for analysing a wide variety of queu...
New Features: Weighted Communities algorithm Chinese Whispers algorithm Siblinarity Antichain algor...
A minor release with bug fixes and some enhancements. New features Objects of class microNetPr...
Release 3.11.0 Major features and improvements Allow the selection and filtering of nodes by modula...
The octave queueing package is a collection of functions written in GNU Octave for analyzing queuein...
Release 3.8.0 Major features and improvements Finish the new node metadata side panel, which allows...
The main improvement in version 2.1 is that now support the Keras functional API in the Graph and Li...
Updated network generation which is only compatible with Mathematica v13+. Random batch training wi...
With the 2.0 release, NNGT moves from inheritance to composition for the underlying graphs. This ena...
New Features Processing Nodes: Networks can now have an arbitrary numbers of node layers. The first...
In this release we are pleased to announce that local operations and classical communication (LOCC) ...
Breaking Changes Network settings are now stored in a 1D list rather than the nested ragged array s...
Features: shannon_entropy_cost_fn : optimize shannon entropy in network added __version__ attribute...
In this release we add a few new utilities and ansatzes. state_vec_fn : constructs a function that ...
comment: the increment to `0.3.` is not due to a new feature*. For now on we increment the second nu...
The Queueing Network Analysis Tool (QNAT) is a powerful package for analysing a wide variety of queu...
New Features: Weighted Communities algorithm Chinese Whispers algorithm Siblinarity Antichain algor...
A minor release with bug fixes and some enhancements. New features Objects of class microNetPr...
Release 3.11.0 Major features and improvements Allow the selection and filtering of nodes by modula...
The octave queueing package is a collection of functions written in GNU Octave for analyzing queuein...
Release 3.8.0 Major features and improvements Finish the new node metadata side panel, which allows...
The main improvement in version 2.1 is that now support the Keras functional API in the Graph and Li...
Updated network generation which is only compatible with Mathematica v13+. Random batch training wi...
With the 2.0 release, NNGT moves from inheritance to composition for the underlying graphs. This ena...