Add Exact TN to docs (#1021)

### Before submitting Please complete the following checklist when submitting a PR: - [ ] All new features must include a unit test. If you've fixed a bug or added code that should be tested, add a test to the [`tests`](../tests) directory! - [ ] All new functions and code must be clearly commented and documented. If you do make documentation changes, make sure that the docs build and render correctly by running `make docs`. - [ ] Ensure that the test suite passes, by running `make test`. - [x] Add a new entry to the `.github/CHANGELOG.md` file, summarizing the change, and including a link back to the PR. - [x] Ensure that code is properly formatted by running `make format`. When all the above are checked, delete everything above the dashed line and fill in the pull request template. ------------------------------------------------------------------------------------------------------------ **Context:** [sc-72893] **Description of the Change:** **Benefits:** **Possible Drawbacks:** **Related GitHub Issues:** --------- Co-authored-by: ringo-but-quantum <[email protected]> Co-authored-by: Ali Asadi <[email protected]> Co-authored-by: ANTH0NY <[email protected]>
PennyLaneAI · Dec 13, 2024 · e9a3b80 · e9a3b80
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diff --git a/.github/CHANGELOG.md b/.github/CHANGELOG.md
@@ -84,6 +84,9 @@
 
 ### Documentation
 
+* Add the exact tensor network to the Lightning-Tensor docs.
+  [(#1021)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1021)
+
 * Update Lightning-Tensor installation docs and usage suggestions.
   [(#979)](https://github.com/PennyLaneAI/pennylane-lightning/pull/979)
 

diff --git a/doc/lightning_tensor/device.rst b/doc/lightning_tensor/device.rst
@@ -1,35 +1,27 @@
 Lightning Tensor device
 =======================
 
-The ``lightning.tensor`` device is a tensor network simulator device. The device is built on top of the `cutensornet <https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/index.html>`__ from the NVIDIA cuQuantum SDK, enabling GPU-accelerated simulation of quantum tensor network evolution. This device is designed to simulate large-scale quantum circuits using tensor networks. For small circuits, state-vector simulator plugins may be more suitable.
+The ``lightning.tensor`` device is a tensor network simulator, supporting both the Matrix Product State (MPS) and Exact Tensor Network methods. The device is built on top of the `cutensornet <https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/index.html>`__ from the NVIDIA cuQuantum SDK, enabling GPU-accelerated simulation of quantum tensor network evolution. This device is designed to simulate large-scale quantum circuits using tensor networks. For small circuits, state-vector simulator plugins may be more suitable.
 
-A ``lightning.tensor`` device can be loaded simply using:
+The ``lightning.tensor`` device defaults to the Matrix Product State (MPS) method, and can be loaded using:
 
 .. code-block:: python
 
     import pennylane as qml
     dev = qml.device("lightning.tensor", wires=100)
 
-By default, the device represents the quantum state approximated as a Matrix Product State (MPS).
+By default, the device approximates the quantum state using an MPS.
 The default setup for the MPS tensor network approximation is:
     - ``max_bond_dim`` (maximum bond dimension) defaults to ``128`` .
     - ``cutoff`` (singular value truncation threshold) defaults to ``0`` .
     - ``cutoff_mode`` (singular value truncation mode) defaults to ``abs`` , considering the absolute values of the singular values; Alternatively, users can opt to set ``cutoff_mode`` to ``rel`` to consider the relative values of the singular values.
-Note that the ``cutensornet`` will automatically determine the reduced extent of the bond dimension based on the lowest among the multiple truncation cutoffs (``max_bond_dim``, ``cutoff-abs`` and ``cutoff-rel``). For more details on how the ``cutoff`` works, please check it out the `cuQuantum documentation <https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/api/types.html#cutensornettensorsvdconfigattributes-t>`__.
+Note that the ``cutensornet`` will automatically determine the reduced extent of the bond dimension based on the lowest among the multiple truncation cutoffs (``max_bond_dim``, ``cutoff-abs`` and ``cutoff-rel``). For more details on how the ``cutoff`` works, please check the `cuQuantum documentation <https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/api/types.html#cutensornettensorsvdconfigattributes-t>`__.
 
-The ``lightning.tensor`` device dispatches all operations to be performed on a CUDA-capable GPU of generation SM 7.0 (Volta)
-and greater. This device supports both exact and finite shots measurements. Currently, the supported differentiation methods are parameter-shift and finite-diff. Note that the MPS backend of lightning.tensor supports multi-wire gates via Matrix Product Operators (MPO).
-
-The ``lightning.tensor`` device is designed for expectation value calculations. Measurements of ``qml.probs()`` or ``qml.state()`` return dense vectors of dimension :math:`2^{n_\text{qubits}}`, so they should only be used for small systems.
-
-.. note:: ``qml.Hermitian`` is currently only supported for single wires. You can use ``qml.pauli_decompose`` on smaller matrices to obtain a compatible Pauli decomposition in the meantime.
-
-Users also have the flexibility to customize these parameters according to their specific needs with:
+Users also have the flexibility to customize MPS parameters according to their specific needs with:
 
 .. code-block:: python
     
     import pennylane as qml
-    import numpy as np
     
     num_qubits = 100
 
@@ -41,7 +33,21 @@ Users also have the flexibility to customize these parameters according to their
 
     dev = qml.device("lightning.tensor", wires=num_qubits, method="mps", **device_kwargs_mps)
 
-The ``lightning.tensor`` device allows users to get quantum circuit gradients using the ``parameter-shift`` method. This can be enabled at the PennyLane ``QNode`` level with:
+Users can also run the ``lightning.tensor`` device in the **Exact Tensor Network** mode by setting the ``method`` argument to ``"tn"``:
+
+.. code-block:: python
+
+    import pennylane as qml
+    dev = qml.device("lightning.tensor", wires=100, method="tn")
+
+The lightning.tensor device dispatches all operations to be performed on a CUDA-capable GPU of generation SM 7.0+ (Volta and later)
+and greater. This device supports both exact and finite shots measurements. Currently, the supported differentiation methods are parameter-shift and finite-diff. Note that the MPS backend of ``lightning.tensor`` supports multi-wire gates via Matrix Product Operators (MPO).
+
+The ``lightning.tensor`` device is designed for expectation value calculations. Measurements of :func:`~pennylane.probs` or :func:`~pennylane.state` return dense vectors of dimension :math:`2^{\text{n_qubits}}`, so they should only be used for small systems.
+
+.. note:: Currently only single-wire :class:`~pennylane.Hermitian` observables are supported. You can use :func:`~pennylane.pauli_decompose` on smaller matrices to obtain a compatible Pauli decomposition in the meantime.
+
+The ``lightning.tensor`` device allows users to get quantum circuit gradients using the :func:`~pennylane.gradients.param_shift` method. This can be enabled at the PennyLane :class:`~pennylane.QNode` level with:
 
 .. code-block:: python
 
@@ -59,7 +65,7 @@ Note that as ``lightning.tensor`` cannot be cleaned up like other state-vector d
 Operations and observables support
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The "lightning.tensor" supports all gate operations supported by PennyLane.
+The ``lightning.tensor`` supports all gate operations supported by PennyLane, with the exception of :class:`~pennylane.StatePrep`, which is *not supported* by the *Exact Tensor Network* method. 
 
 **Supported operations:**
 
@@ -128,9 +134,9 @@ The "lightning.tensor" supports all gate operations supported by PennyLane.
 
 **Supported observables:**
 
-The ``lightning.tensor`` supports all observables supported by the Lightning state-vector simulators, besides ``qml.SparseHamiltonian``, ``qml.Projector`` and limited support to ``qml.Hamiltonian``, ``qml.Prod`` since ``lightning.tensor`` only supports 1-wire Hermitian observables.
+The ``lightning.tensor`` supports all observables supported by the Lightning state-vector simulators, besides :class:`~pennylane.SparseHamiltonian`, :class:`~pennylane.Projector` and limited support to :class:`~pennylane.ops.op_math.Sum`, :class:`~pennylane.ops.op_math.Prod` since ``lightning.tensor`` only supports 1-wire :class:`~pennylane.Hermitian` observables.
 
-Users can not create a ``Hamiltonian`` or ``Prod`` observable from ``Hamiltonian`` observables.
+Users cannot create a :class:`~pennylane.ops.op_math.Sum` observable or a :class:`~pennylane.ops.op_math.Prod` observable from :class:`~pennylane.ops.op_math.Sum` observables.
 
 
 
@@ -143,7 +149,6 @@ Users can not create a ``Hamiltonian`` or ``Prod`` observable from ``Hamiltonian
 
     ~pennylane.ops.op_math.Exp
     ~pennylane.Hadamard
-    ~pennylane.Hamiltonian
     ~pennylane.Hermitian
     ~pennylane.Identity
     ~pennylane.PauliX

diff --git a/pennylane_lightning/core/_version.py b/pennylane_lightning/core/_version.py
@@ -16,4 +16,4 @@
    Version number (major.minor.patch[-label])
 """
 
-__version__ = "0.40.0-dev35"
+__version__ = "0.40.0-dev36"