TensorNetwork backend for QuTiP.

[AGaliciaMartinez]

I'm currently implementing a TensorNetwork backend for QuTiP [1, 2]. I'd like to use the QuOperator included in TensorNetwork [3], but I see it is not included in the main documentation page and it is not importable because its package folder is lacking an __init__ file.

What is the status of QuOperator?

If the intention is to support it, I can offer some help maintaining it if there is a core developer who can review pull requests and answer questions.

If the intention is to remove it, would you mind if I vendored it into qutip-tensornetwork and evolved it myself from there?

I am also pinging @hodgestar and @nwlambert as the are helping me with the package.

[1] https://qutip.org [2] https://github.com/qutip/qutip-tensornetwork [3] https://github.com/google/TensorNetwork/tree/master/tensornetwork/quantum

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Some background on the project:

Currently QuTiP is in its 4-th version but the new, actively developed QuTiP 5, brings some interesting changes. In particular, QuTiP will now able to support different backends. Examples of these backeds are the native Dense and CSR classes that are similar to the ndarray of NumPy and csr of SciPy. Recently, during the Google Summer of Code of 2021, the development of two new backends, qutip-tensorflow and qutip-cupy, was started which brought the possibility to work with GPU's in QuTiP, among other things.

Our goal is to create a qutip-tensornetwork plug-in for QuTiP using TensorNetwork as backend. I was interested in your approach to build tensor networks and I was very excited to see that you support different backends. We expect that a tensor network representation of some operations will be very useful when simulating quantum circuits with qutip-qip as with tensor networks we can describe local operations efficiently, both memory and computation wise. We also hope that this new package, qutip-tensornetwork, proves itself useful describing the operations one encounters in open quantum systems and we further aim to eventually use tensor networks to solve large dynamical open quantum systems more efficiently.