Comments (3)
To whom might be interested, I have been maintaining a forked version of the package under the name "tensornetwork-ng" which can be found at this GitHub repository: https://github.com/refraction-ray/TensorNetwork-NG. The main motivation behind this fork is to ensure the robustness of the downstream project, TensorCircuit, and to ensure its compatibility with future breaking changes in Python, NumPy, and JAX.
I must admit that due to limited resources and effort, I have only been able to maintain certain submodules while temporarily pruning others (irrelevant from tensorcircuit). However, I would be more than happy to welcome and encourage anyone interested to participate and contribute and I look forward to any potential collaborations and contributions :)
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@VolodyaCO , TensorNetwork is a great package and I really appreciate the ML backend agnostic design. The main reason I chose to use this package is because, at the time I began working on TensorCircuit in 2020, TensorNetwork was being actively maintained and well-supported.
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Thanks for your answer @refraction-ray. Indeed, my main interest is tensorcircuit. I see that in tensorcircuit people have been overriding some parts of the JAX backend from tensornetwork, but I was wondering why tensorcircuit was relying on an unmaintained package.
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Related Issues (20)
- Flaky Test when seed not used HOT 5
- Flaky test (always fails) when seeds are removed HOT 1
- Test `test_gmres_on_larger_random_problem` fails without seeds
- symmetric backend very slow for PEPS tensors HOT 4
- Home Network
- lack sqrt operation in tensornetwork/matrixproductstates/infinite_mps.py
- TensorNetwork backend for QuTiP. HOT 2
- Bug for numpy backend ``sum`` method HOT 3
- SVD on jax backend and thus ``split_node`` cannot be jitted when ``max_truncation_err`` is set
- Bug of setting `center_position` in `apply_two_site_gate` when there's no truncation
- Quantum hardware system integration with TensorNetwork
- Missing code for TensorNetwork Machine Learning HOT 1
- `backend.item` in MPS calculation is incompatible with autograd in jax HOT 2
- The lack of tensor-train RNNs for latest tf/keras HOT 1
- Parallelism Contractors HOT 1
- Question: Vector to FiniteMPS?
- Is there a simple way to multiply a scalar to the tensor values of a node that is part of a network of tensors? HOT 2
- Tensor
- Pf
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