Adding Efrat's edits 2/4

Merge branch 'backends' of https://github.com/mawolf2023/cuda-quantum into backends

docs/sphinx/using/backends/sims/photonics.rst

@@ -202,7 +202,7 @@ Hong-Ou-Mandel effect.
 
 Executing Photonics Kernels
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-In order to execute a photonics kernel, you need to specific a photonics simulator backend like :code:`orca-photonics` used in the example below. 
+In order to execute a photonics kernel, you need to specify a photonics simulator backend like :code:`orca-photonics` used in the example below. 
 There are two ways to execute photonics kernels :code:`sample` and :code:`get_state`
 
 
@@ -282,7 +282,7 @@ The :code:`get_state` command can be used to generate statistics about the quant
         # Compute the statevector of the kernel
         result = cudaq.get_state(kernel, qumode_count)
 
-        print(np.array(result))k
+        print(np.array(result))
 
 
 .. parsed-literal::

docs/sphinx/using/examples/multi_gpu_workflows.rst

@@ -28,7 +28,7 @@ You can run a state vector simulation using your CPU with the :code:`qpp-cpu` ba
 
     { 00:475 11:525 }
 
-As the number of qubits increases to even modest size, the CPU simulation will become impractically slow.  By switching to the :code:`nvidia` backend, you can accelerate the same code on a single GPU and achieve a speedup of up to **2500x**.  If you have a GPU available, this the default backend to ensure maximum productivity.
+As the number of qubits increases to even modest size, the CPU simulation will become impractically slow.  By switching to the :code:`nvidia` backend, you can accelerate the same code on a single GPU and achieve a speedup of up to **425x**.  If you have a GPU available, this the default backend to ensure maximum productivity.
 
 .. literalinclude:: ../../snippets/python/using/examples/multi_gpu_workflows/multiple_targets.py
     :language: python
@@ -69,7 +69,7 @@ Parallel execution over multiple QPUs (`mqpu`)
 Batching Hamiltonian Terms 
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Multiple GPUs can also come in handy for cases where applications might benefit from multiple QPUs running asynchronously.  The `mqpu` backend uses multiple GPUs to simulate each QPU so you can test and accelerate quantum applications with parallelization.
+Multiple GPUs can also come in handy for cases where applications might benefit from multiple QPUs running in parallel.  The `mqpu` backend uses multiple GPUs to simulate QPUs so you can accelerate quantum applications with parallelization.
 
 
 .. image:: images/mqpu.png
@@ -152,7 +152,7 @@ Multi-QPU + Other Backends (`remote-mqpu`)
 -------------------------------------------
 
 
-The `mqpu` backend can be extended so that each parallel simulated QPU can be simulated with backends other than :code:`nvidia`.  This provides a way to simulate larger scale circuits and execute parallel algorithms. This accomplished by launching remotes servers which each simulated a QPU.  
+The `mqpu` backend can be extended so that each parallel simulated QPU run backends other than :code:`nvidia`.  This provides a way to simulate larger scale circuits and execute parallel algorithms. This accomplished by launching remotes servers which each simulated a QPU.  
 The code example below demonstrates this using the :code:`tensornet-mps` backend which allows sampling of a 40 qubit circuit too larger for state vector simulation. In this case, the target is specified as :code:`remote-mqpu` while an additional :code:`backend` is specified for the simulator used for each QPU.  
 
 The default approach uses one GPU per QPU and can both launch and close each server automatically. This is accomplished by specifying :code:`auto_launch` and :code"`url` within :code:`cudaq.set_target`.  Running the script below will then sample the 40 qubit circuit using two QPUs each running :code:`tensornet-mps`.