Docs preview for PR #832.

NVIDIA · Nov 13, 2023 · 0d3d513 · 0d3d513
1 parent 11d258a
commit 0d3d513
Show file tree

Hide file tree

Showing 99 changed files with 3,501 additions and 893 deletions.
diff --git a/pr-832/CMakeLists.txt b/pr-832/CMakeLists.txt
@@ -50,7 +50,8 @@ add_nvqpp_test(IterativePhaseEstimation other/iterative_qpe.cpp)
 add_nvqpp_test(RandomWalkPhaseEstimation other/random_walk_qpe.cpp)
 
 if (CUSTATEVEC_ROOT AND CUDA_FOUND) 
-  add_nvqpp_test(CuQuantumBackend basics/cuquantum_backends.cpp TARGET nvidia)  
+  add_nvqpp_test(CuQuantumGHZ basics/cuquantum_backends.cpp TARGET nvidia)  
+  add_nvqpp_test(CuQuantumBernsteinVazirani algorithms/bernstein_vazirani.cpp TARGET nvidia)  
 endif()
 
 # mqpu code snippets, needs custatevec backend and (optionally MPI)

diff --git a/pr-832/_images/attachToDevContainer.png b/pr-832/_images/attachToDevContainer.png
diff --git a/pr-832/_images/devContainersExtension.png b/pr-832/_images/devContainersExtension.png
diff --git a/...n_tutorials_single_qubit_rotation_8_1.png → ...ython_tutorials_cost_minimization_9_1.png b/...n_tutorials_single_qubit_rotation_8_1.png → ...ython_tutorials_cost_minimization_9_1.png
diff --git a/...les_python_tutorials_hybrid_qnns_10_1.png → ...les_python_tutorials_hybrid_qnns_11_1.png b/...les_python_tutorials_hybrid_qnns_10_1.png → ...les_python_tutorials_hybrid_qnns_11_1.png
diff --git a/pr-832/_images/openTerminal.png b/pr-832/_images/openTerminal.png
diff --git a/...hon/tutorials/single_qubit_rotation.ipynb → ...hon/tutorials/cost_minimization.ipynb.txt b/...hon/tutorials/single_qubit_rotation.ipynb → ...hon/tutorials/cost_minimization.ipynb.txt
@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Single Qubit Rotation \n",
+    "# Cost Minimization\n",
     "\n",
     "Below we start with a basic example of a hybrid variational algorithm which involves flipping the bloch vector of a qubit from the $\\ket{0}$ to the $\\ket{1}$ state. First we import the relevant packages and set our backend to simulate our workflow on NVIDIA GPUs. \n",
     "\n",
@@ -64,8 +64,6 @@
    "outputs": [],
    "source": [
     "cost_values = []\n",
-    "cost_values.append(initial_cost_value)\n",
-    "\n",
     "\n",
     "def cost(parameters):\n",
     "    \"\"\"Returns the expectation value as our cost.\"\"\"\n",
@@ -115,6 +113,15 @@
     "result = optimizer.optimize(dimensions=2, function=cost)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install matplotlib"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 19,
@@ -147,7 +154,6 @@
    ],
    "source": [
     "# Plotting how the value of the cost function decreases during the minimization procedure.\n",
-    "# !pip install matplotlib\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "x_values = list(range(len(cost_values)))\n",

diff --git a/pr-832/_sources/examples/python/tutorials/executing_circuits.ipynb.txt b/pr-832/_sources/examples/python/tutorials/executing_circuits.ipynb.txt
@@ -33,7 +33,7 @@
     "qubit_count = 2\n",
     "\n",
     "# Define the simulation target.\n",
-    "cudaq.set_target(\"nvidia\")\n",
+    "cudaq.set_target(\"qpp-cpu\")\n",
     "\n",
     "# Define a quantum kernel function.\n",
     "kernel = cudaq.make_kernel()\n",
@@ -87,7 +87,7 @@
     "qubit_count = 2\n",
     "\n",
     "# Define the simulation target.\n",
-    "cudaq.set_target(\"nvidia\")\n",
+    "cudaq.set_target(\"qpp-cpu\")\n",
     "\n",
     "# Define a quantum kernel function.\n",
     "kernel = cudaq.make_kernel()\n",

diff --git a/pr-832/_sources/examples/python/tutorials/hybrid_qnns.ipynb.txt b/pr-832/_sources/examples/python/tutorials/hybrid_qnns.ipynb.txt
@@ -22,6 +22,15 @@
     "We perform binary classification on the MNIST dataset where data flows through the neural network architecture to the quantum circuit whose output is used to classify hand written digits."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install matplotlib torch torchvision"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -30,8 +39,6 @@
    "source": [
     "# Import the relevant packages\n",
     "\n",
-    "# !pip install matplotlib torch torchvision\n",
-    "\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "\n",

diff --git a/pr-832/_sources/examples/python/tutorials/multi_gpu_workflows.ipynb.txt b/pr-832/_sources/examples/python/tutorials/multi_gpu_workflows.ipynb.txt
@@ -32,7 +32,7 @@
    "source": [
     "### Available Targets\n",
     "\n",
-    "- **default**: The default qpp based CPU backend which is multithreaded to maximise the usage of available cores on your system.\n",
+    "- **qpp-cpu**: The qpp based CPU backend which is multithreaded to maximize the usage of available cores on your system.\n",
     "\n",
     "- **nvidia**: GPU based backend which accelerates quantum circuit simulation on NVIDIA GPUs powered by cuQuantum.\n",
     "\n",
@@ -76,7 +76,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Default CPU Backend"
+    "### QPP-based CPU Backend"
    ]
   },
   {
@@ -93,7 +93,7 @@
     }
    ],
    "source": [
-    "cpu_result = ghz_state(n_qubits=2, target=\"default\")\n",
+    "cpu_result = ghz_state(qubit_count=2, target=\"qpp-cpu\")\n",
     "\n",
     "cpu_result.dump()"
    ]
@@ -122,7 +122,7 @@
     }
    ],
    "source": [
-    "gpu_result = ghz_state(n_qubits=25, target=\"nvidia\")\n",
+    "gpu_result = ghz_state(qubit_count=25, target=\"nvidia\")\n",
     "\n",
     "gpu_result.dump()"
    ]

diff --git a/pr-832/_sources/examples/python/tutorials/vqe.ipynb.txt b/pr-832/_sources/examples/python/tutorials/vqe.ipynb.txt
@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# !pip install openfermionpyscf"
+    "%pip install openfermionpyscf"
    ]
   },
   {

diff --git a/pr-832/_sources/index.rst.txt b/pr-832/_sources/index.rst.txt
@@ -5,8 +5,7 @@ CUDA Quantum
 Welcome to the CUDA Quantum documentation page!
 You are browsing the documentation for |version| version of CUDA Quantum. You can find documentation for all released versions :doc:`here <versions>`.
 
-**CUDA Quantum** is a single-source, modern C++ programming model and compiler platform 
-for the quantum acceleration of existing heterogeneous computing architectures. 
+**CUDA Quantum** is a programming model and toolchain for using quantum acceleration in heterogeneous computing architectures available in C++ and Python.
 
 .. toctree::
    :caption: Contents