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    "# Optimizing Performance\n",
    "\n",
    "Performance is a key focus for the CUDA-Q design.  This section highlights some features that advanced users can take advantage of to increase performance in certain situations."
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    "### Gate Fusion\n",
    "\n",
    "Gate fusion is an optimization technique where consecutive gates are combined into a single gate operation to improve the efficiency of the simulation (See figure below).  By targeting the `nvidia-mgpu` backend and setting the `CUDAQ_MGPU_FUSE` environment variable, you can select the degree of fusion that takes place. A full command line example would look like `CUDAQ_MGPU_FUSE=4 python c2h2VQE.py --target nvidia --target-option fp64,mgpu`\n",
    "\n",
    "<img src=\"../images/gate-fuse.png\" style=\"width:400px;\">\n",
    "\n",
    "The importance of gate fusion is system dependent, but can have a large influence on the performance of the simulation.  See the example below for a 24 qubit VQE experiment where changing the fusion level resulted in significant performance boosts.\n",
    "\n",
    "<img src=\"../images/gatefusion.png\" style=\"width:300px;\">\n",
    "\n"
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