Course intro updated

dnnagy · May 7, 2019 · 01c050c · 01c050c
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diff --git a/cirq_version/00_Course_Introduction.ipynb b/cirq_version/00_Course_Introduction.ipynb
@@ -16,7 +16,7 @@
     "\n",
     "3. **Classical-quantum hybrid learning algorithms**. Encoding classical information in quantum systems. Discrete optimization in machine learning. Variational models in unsupervised learning. Kernel methods. Sampling and probabilistic models. \n",
     "\n",
-    "4. **Coherent learning protocols**. Quantum phase estimation and quantum matrix inversion. Gaussian processes on a quantum computer. Self-exponentiation and quantum principal component analysis. Preparing a Gram matrix.\n",
+    "4. **Coherent learning protocols**. Quantum Fourier transformation, quantum phase estimation, and quantum matrix inversion. Basic linear algebra subroutines by quantum algorithms. Gaussian processes on a quantum computer.\n",
     "\n",
     "Quantum computing has two main paradigms, the gate model and quantum annealing:\n",
     "\n",
@@ -36,7 +36,7 @@
     "\n",
     "Almost all packages can be installed with conda: `conda install jupyter matplotlib networkx numpy scikit-learn scipy`.\n",
     "\n",
-    "The only packages not available are the ones produced by quantum hardware vendors. We will use many of their packages. You can install these with pip: `pip install cirq dwave-networkx dimod minorminer qiskit qiskit-aqua`. As a quick sanity check, if you can execute the following cell without error messages, you should not face problems with the rest of the notebooks:"
+    "The only packages not available are the ones produced by quantum hardware vendors. We will use many of their packages. You can install these with pip: `pip install cirq dwave-networkx dimod minorminer`. As a quick sanity check, if you can execute the following cell without error messages, you should not face problems with the rest of the notebooks:"
    ]
   },
   {
@@ -59,9 +59,7 @@
     "import cirq\n",
     "import dwave_networkx\n",
     "import dimod\n",
-    "import minorminer\n",
-    "import qiskit\n",
-    "import qiskit_aqua"
+    "import minorminer"
    ]
   }
  ],
@@ -81,7 +79,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,

diff --git a/cirq_version/13_Quantum_Phase_Estimation.ipynb b/cirq_version/13_Quantum_Phase_Estimation.ipynb
@@ -19,13 +19,13 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [], 
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import cirq\n",
     "from cirq import Circuit\n"
    ]
-  }, 
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -147,7 +147,7 @@
     "    cirq.H(q0),\n",
     "    cirq.H(q1)\n",
     ")"
-  ]
+   ]
   },
   {
    "cell_type": "markdown",
@@ -189,7 +189,7 @@
     "    cirq.measure(q0, key='q0'),\n",
     "    cirq.measure(q1, key='q1')\n",
     "])\n",
-   "plot_circuit(qpe)"
+    "plot_circuit(qpe)"
    ]
   },
   {

diff --git a/forest_version/00_Course_Introduction.ipynb b/forest_version/00_Course_Introduction.ipynb
@@ -16,7 +16,7 @@
     "\n",
     "3. **Classical-quantum hybrid learning algorithms**. Encoding classical information in quantum systems. Discrete optimization in machine learning. Variational models in unsupervised learning. Kernel methods. Sampling and probabilistic models. \n",
     "\n",
-    "4. **Coherent learning protocols**. Quantum phase estimation and quantum matrix inversion. Gaussian processes on a quantum computer. Self-exponentiation and quantum principal component analysis. Preparing a Gram matrix.\n",
+    "4. **Coherent learning protocols**. Quantum Fourier transformation, quantum phase estimation, and quantum matrix inversion. Basic linear algebra subroutines by quantum algorithms. Gaussian processes on a quantum computer.\n",
     "\n",
     "Quantum computing has two main paradigms, the gate model and quantum annealing:\n",
     "\n",
@@ -36,7 +36,7 @@
     "\n",
     "Almost all packages can be installed with conda: `conda install jupyter matplotlib networkx numpy scikit-learn scipy`.\n",
     "\n",
-    "The only packages not available are the ones produced by quantum hardware vendors. We will use many of their packages. You can install these with pip: `pip install cirq dwave-networkx dimod minorminer qiskit qiskit-aqua`. As a quick sanity check, if you can execute the following cell without error messages, you should not face problems with the rest of the notebooks:"
+    "The only packages not available are the ones produced by quantum hardware vendors. We will use many of their packages. You can install these with pip: `pip install dwave-networkx dimod minorminer pyquil quantum-grove`. As a quick sanity check, if you can execute the following cell without error messages, you should not face problems with the rest of the notebooks:"
    ]
   },
   {
@@ -56,12 +56,11 @@
     "import sklearn\n",
     "import scipy\n",
     "\n",
-    "import cirq\n",
     "import dwave_networkx\n",
     "import dimod\n",
     "import minorminer\n",
-    "import qiskit\n",
-    "import qiskit_aqua"
+    "import pyquil\n",
+    "import grove"
    ]
   }
  ],
@@ -81,7 +80,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,

diff --git a/qiskit_version/00_Course_Introduction.ipynb b/qiskit_version/00_Course_Introduction.ipynb
@@ -16,7 +16,7 @@
     "\n",
     "3. **Classical-quantum hybrid learning algorithms**. Encoding classical information in quantum systems. Discrete optimization in machine learning. Variational models in unsupervised learning. Kernel methods. Sampling and probabilistic models. \n",
     "\n",
-    "4. **Coherent learning protocols**. Quantum phase estimation and quantum matrix inversion. Gaussian processes on a quantum computer. Self-exponentiation and quantum principal component analysis. Preparing a Gram matrix.\n",
+    "4. **Coherent learning protocols**. Quantum Fourier transformation, quantum phase estimation, and quantum matrix inversion. Basic linear algebra subroutines by quantum algorithms. Gaussian processes on a quantum computer.\n",
     "\n",
     "Quantum computing has two main paradigms, the gate model and quantum annealing:\n",
     "\n",
@@ -36,7 +36,7 @@
     "\n",
     "Almost all packages can be installed with conda: `conda install jupyter matplotlib networkx numpy scikit-learn scipy`.\n",
     "\n",
-    "The only packages not available are the ones produced by quantum hardware vendors. We will use many of their packages. You can install these with pip: `pip install cirq dwave-networkx dimod minorminer qiskit qiskit-aqua`. As a quick sanity check, if you can execute the following cell without error messages, you should not face problems with the rest of the notebooks:"
+    "The only packages not available are the ones produced by quantum hardware vendors. We will use many of their packages. You can install these with pip: `pip install dwave-networkx dimod minorminer qiskit qiskit-aqua`. As a quick sanity check, if you can execute the following cell without error messages, you should not face problems with the rest of the notebooks:"
    ]
   },
   {
@@ -56,12 +56,11 @@
     "import sklearn\n",
     "import scipy\n",
     "\n",
-    "import cirq\n",
     "import dwave_networkx\n",
     "import dimod\n",
     "import minorminer\n",
     "import qiskit\n",
-    "import qiskit_aqua"
+    "import qiskit.aqua"
    ]
   }
  ],
@@ -81,7 +80,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,