Update references to new notebook

Author: rmshaffer

README.md

@@ -312,7 +312,11 @@ This folder contains examples that illustrate the use of Amazon Braket Hybrid Jo
 
 - [**Parallel simulations on multiple GPUs**](examples/nvidia_cuda_q/2_parallel_simulations.ipynb)
 
-  This tutorial shows you how to parallelize the simulations of observables and circuit batches over multiple GPUs using Braket Hybrid Jobs.
+  This tutorial shows you how to parallelize the simulations of observables and circuit batches over multiple GPUs using CUDA-Q with Braket Hybrid Jobs.
+
+- [**Distributed state vector simulations on multiple GPUs (advanced)**](examples/nvidia_cuda_q/3_distributed_statevector_simulations.ipynb)
+
+  This tutorial shows you how to distribute a single state vector simulation across multiple GPUs using CUDA-Q with Braket Hybrid Jobs.
 
 ---
 

examples/nvidia_cuda_q/0_hello_cudaq_jobs.ipynb

@@ -260,7 +260,7 @@
    "metadata": {},
    "source": [
     "## Summary\n",
-    "This notebook shows you how to run your first CUDA-Q program with Amazon Braket Hybrid Jobs. Using the BYOC feature of Amazon Braket and a shell script we provide, you can create a CUDA-Q environment with a few lines of code. Once you have registered your CUDA-Q container image, you can run CUDA-Q programs with Braket Hybrid Jobs and scale your workloads up and out with the range of compute options provided by AWS. In the following tutorials, we will show you how to run CUDA-Q simulations on GPUs ([notebook](1_simulation_with_GPUs.ipynb)) and distribute workloads across multiple instances ([notebook](2_parallel_simulations.ipynb))."
+    "This notebook shows you how to run your first CUDA-Q program with Amazon Braket Hybrid Jobs. Using the BYOC feature of Amazon Braket and a shell script we provide, you can create a CUDA-Q environment with a few lines of code. Once you have registered your CUDA-Q container image, you can run CUDA-Q programs with Braket Hybrid Jobs and scale your workloads up and out with the range of compute options provided by AWS. In the following tutorials, we will show you how to run CUDA-Q simulations on GPUs ([notebook](1_simulation_with_GPUs.ipynb)), distribute workloads across multiple instances ([notebook](2_parallel_simulations.ipynb)), and distribute a single state vector simulation across multiple GPUs ([notebook](3_distributed_statevector_simulations.ipynb))."
    ]
   },
   {

test/integ_tests/test_all_notebooks.py

@@ -31,6 +31,7 @@
     "0_hello_cudaq_jobs.ipynb",
     "1_simulation_with_GPUs.ipynb",
     "2_parallel_simulations.ipynb",
+    "3_distributed_statevector_simulations.ipynb",
     # Notebooks that require devices to be online
     "Randomness_Generation.ipynb",
     "Allocating_Qubits_on_QPU_Devices.ipynb",