Comments (5)
Could you provide a code sample of your ideal workflow?
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If your network creation is wrapped in a function, you should just be able to regenerate a TN with whatever tensors you want.
from tensornetwork.
Yeah, that's true. And the (negligible) cost of doing so would be about the same as the copy, I expect.
from tensornetwork.
Never mind :)
from tensornetwork.
I do want to build a TensorNetworkShell
class that doesn't have tensors. That would be useful for contraction algorithms.
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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
- Maintenance of this repository HOT 3
- Tensor
- Pf
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