hybridq
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HybridQ is a highly extensible platform designed to provide a common framework to integrate multiple state-of-the-art techniques to simulate large scale quantum circuits on a variety of hardware. Hybr...
While installing the hybridq module in Python 3.9, there is an error 'metadata-generation-failed' and impedes the successful installation. Request the creators to shed light on this issue!
Looks like this was updated to support python 3.10 but the PyPi latest version was not updated when this change was made, can you release a new version that includes...
Running into this error message during installation with `conda env create -f environment.yml` ``` Collecting cotengra@ git+https://github.com/jcmgray/cotengra@master (from hybridq==0.8.2->-r /home/drodriguezperez/repos/hybridq/condaenv.ssrd97gp.requirements.txt (line 1)) Cloning https://github.com/jcmgray/cotengra (to revision master) to /tmp/pip-install-t310ja9q/cotengra_1d609ab9e2134b9ab9c2fbbaf43f12b2 Pip...
For performance reasons, `simulate` may use either a single array of complex numbers (when using `optimize=evolution-einsum` or `backend='jax')` or a pair or real valued arrays (when using `optimize=evolution-hybridq`). For this...
When we have a diagonal Kraus representation, such as sum_i s_i K_i rho K_i^dag, we should be able to sample, assuming that sum_i s_i K_i^dag K_i = Identity. If on...
At the moment, parameters for `cotengra` are not optimized for the input circuit. An autotuner would allow to select the right parameters to get the best contraction without the user's...