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+# Quantum Computing - Toolkit Information
+
+# CUDA Quantum Version
+
+This directory describes the requirements and operational conventions for using CUDA Quantum as the programming environment for running the benchmark programs contained in the QC-App-Oriented-Benchmarks repository.
+In particular, this document explains how to set up the tools needed to run the CUDA Quantum implementation of these benchmarks.
+
+## Configure a CUDA Quantum Environment
+Create an environment named "cudaq" and then "activate" it using the following commands:
+
+    conda create -n cudaq python=3
+
+    conda activate cudaq
+
+The conda environment is now ready for you to install the CUDA Quantum package.
+
+## Install CUDA Quantum
+
+After activating the conda environment, to ensure you are using the correct installation of `pip`, run the following command:
+
+    pip show pip
+
+If everything is working correctly, the `Location` field should have your newly created environment's name present. For example:
+
+    Location: c:\users\[user]\miniconda\envs\cudaq\lib\site-packages
+
+Enter the following commands to install the latest version of CUDA Quantum and the other required packages.
+
+    pip install numpy matplotlib dwave-cudaq-sdk dwave-neal notebook
+
+You are now ready to run the benchmark programs.
+
+## Configuring Quantum Hardware
+
+The `cudaq` package allows quantum circuits to be run in a real quantum hardware hosted by [D-Wave Leap](https://cloud.dwavesys.com/leap/). 
+
+## Run the benchmark programs in an Anaconda command window.
+
+For example, in an Anaconda command window, you can enter the following commands to change directory to the Ocean MaxCut directory and run the benchmark program:
+
+    cd [your github home directory]\QC-App-Oriented-Benchmarks\maxcut\cudaq
+  
+    python maxcut_benchmark.py
+    
+This will execute the benchmark program and report the benchmark metrics to the console.
+
+The other benchmarks follow a similar format and structure and are executed in the same way (using the appropriate benchmark pgrogram filename).
+
+## Run the benchmark programs in a Jupyter Notebook
+
+Many Python users prefer to execute their Python programs in a Jupyter notebook, which is automatically available with your Anaconda installation.
+Execute the following commands to change directory to one that contains a Jupyter notebook and execute and invoke Jupyter notebook server.
+
+    cd to directory containing jupyter notebook (currently only the maxcut/cudaq directory)
+    jupyter-notebook
+    
+This will then invoke the Jupyter notebook in a new browser tab. There you can copy and paste any of the benchmark program code and execute the programs interactively.
+    
+Note; In some Windows environments, it is necessary to install one additional package (if running a Jupyter notebook results in a Windows "kernel error"):
+
+    conda install pywin32
+
+Once installed, you should be able to successfully start your Jupyter notebook.
+
+## Tested Versions
+
+The repository has been validated on Linux using the following versions as minimums:
+
+    Miniconda Version: 4.10.3
+    Python Versions: 3.8.5 and 3.9.7
+
+Earlier (or later) versions of the software might work without issues, but the benchmark has been specifically validated on these versions. If you have any issues installing, please raise an bug report in the issues tab of the repository.