One-mode MPS NetworkState is rejected despite documented output layout

Summary

NetworkState((2,), dtype="complex128", config=MPSConfig()) rejects a one-mode MPS at construction time. The documented open-boundary MPS layout implies that a one-site output has one physical mode and no left or right bond, so compute_output_state() should return one tensor with shape (2,).

Minimal reproducer

from cuquantum.tensornet.experimental import MPSConfig, NetworkState

with NetworkState((2,), dtype="complex128", config=MPSConfig()) as state:
    output = state.compute_output_state()
    print([tensor.shape for tensor in output])

Actual result:

Traceback (most recent call last):
  File "repro.py", line 3, in <module>
    with NetworkState((2,), dtype="complex128", config=MPSConfig()) as state:
         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File ".../cuquantum/tensornet/experimental/network_state.py", line 187, in __init__
    raise ValueError(f"For system with one physical dimension, please switch to tensor network simulation method via TNConfig")
ValueError: For system with one physical dimension, please switch to tensor network simulation method via TNConfig

No gate is needed to trigger the issue. Applying a one-qubit gate before requesting the output is currently impossible because construction fails.

Expected behavior

Construction succeeds, and compute_output_state() returns a sequence containing one tensor with shape (2,). This report does not make a claim about numerical output beyond that layout.

The compute_output_state() documentation says MPS tensors use pkn order and that the first tensor omits the left bond p while the last tensor omits the right bond n (source). For a tensor that is both first and last, both bonds are absent. set_initial_mps() documents the same open-boundary convention (source).

Source analysis

Current main and release v26.06.0 are both commit a097bf713ca999886a99b53b474439307956f2fd at the time of testing. The constructor explicitly rejects n == 1 with MPSConfig (lines 186-187). A test helper also expands one-qubit cases to two qubits as a workaround (lines 36-38).

Bypassing only the constructor guard in a source-level harness exposes the next failure in _maybe_set_target_state(): both cumulative-product arrays, and therefore max_extents, are empty for n == 1, but max_extents.max() is called (lines 939-945):

ValueError: zero-size array to reduction operation maximum which has no identity

The harness recorded no backend call before this failure. An otherwise identical two-mode control built target shapes [(2, 2), (2, 2)] and reached state_finalize_mps, so the empty reduction is specific to the one-mode extent construction.

A conceptual fix would be to special-case n == 1, or otherwise make target-extent construction robust to zero bonds, producing (state_mode_extents[0],) without reducing or indexing max_extents. Focused tests could cover one-mode output layout with and without max_extent, plus a simple one-qubit gate if supported by the backend.

Environment and limitation

• OS: Linux 7.0.0-28-generic x86_64, glibc 2.43
• Python: 3.13.13
• GPU: NVIDIA GeForce RTX 4080, compute capability 8.9
• Driver: 595.71.05
• Driver-reported CUDA: 13.2
• cuquantum-python-cu13: 26.6.0
• cutensornet-cu13: 2.13.0
• CuPy: 14.1.1
• NumPy: 2.5.1
• nvmath-python: 0.9.0
• cuda-bindings: 13.3.1
• cuda-core: 0.7.0

The official cutensor-cu13==2.7.0 wheel download repeatedly terminated before completion from both PyPI and NVIDIA's index, so I could not execute a full backend run. The public constructor traceback and the two-mode control above were reproduced with narrowly scoped mocks for pre-construction/backend hooks. The tested 26.6.0 wheel's network_state.py SHA-256 exactly matched the current checkout, and both failures occur in Python before GPU/backend execution.

No external code PR was opened because CONTRIBUTING.md states that code contributions are not accepted at this time.

Start in python/cuquantum/tensornet/experimental/network_state.py at the constructor guard around lines 186-187 and _maybe_set_target_state() around lines 939-945. Review the documented output layout and the trajectories_noise/network_state_wrap.py helper, then add focused coverage showing that a one-mode state returns one tensor shaped (2,), including max_extent and a simple gate if supported; done means these cases no longer fail during Python-side setup.