update doc devices

doc/lightning_gpu/device.rst

@@ -142,7 +142,7 @@ Each problem is unique, so it can often be best to choose the default behaviour
 
 **Multi-GPU/multi-node support:**
 
-The ``lightning.gpu`` device allows users to leverage the computational power of many GPUs sitting on separate nodes for running large-scale simulations. 
+The ``lightning.gpu`` device allows users to leverage the computational power of many GPUs distributed across multiple nodes for running large-scale simulations. 
 Provided that NVIDIA ``cuQuantum`` libraries, a ``CUDA-aware MPI`` library and ``mpi4py`` are properly installed and the path to the ``libmpi.so`` is 
 added to the ``LD_LIBRARY_PATH`` environment variable, the following requirements should be met to enable multi-node and multi-GPU simulations:
 

doc/lightning_tensor/device.rst

@@ -1,7 +1,7 @@
 Lightning Tensor device
 =======================
 
-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.
+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>`__ library 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 defaults to the Matrix Product State (MPS) method, and can be loaded using:
 
@@ -15,7 +15,7 @@ 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 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`` or ``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>`__.
 
 Users also have the flexibility to customize MPS parameters according to their specific needs with:
 
@@ -40,7 +40,7 @@ Users can also run the ``lightning.tensor`` device in the **Exact Tensor Network
     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+ 
+The ``lightning.tensor`` device dispatches all operations to be performed on a CUDA-capable GPU of generation SM 7.0 
 and greater (Volta and later). 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.
@@ -57,7 +57,7 @@ The ``lightning.tensor`` device allows users to get quantum circuit gradients us
 
 Check out the :doc:`/lightning_tensor/installation` guide for more information.
 
-Note that as ``lightning.tensor`` cannot be cleaned up like other state-vector devices because the data is attached to the graph. It is recommended to create a new ``lightning.tensor`` device per circuit to ensure resources are correctly handled.
+Note that ``lightning.tensor`` cannot be cleaned up like other state-vector devices since the data is attached to the graph. It is recommended to create a new ``lightning.tensor`` device per circuit to ensure resources are correctly handled.
 
 .. seealso:: `DefaultTensor <https://docs.pennylane.ai/en/latest/code/api/pennylane.devices.default_tensor.DefaultTensor.html>`__ for a CPU only tensor network simulator device.