update

Co-authored-by: Steve Wood <40241007+woodsp-ibm@users.noreply.github.com>

docs/tutorials/04_grover_optimizer.ipynb

@@ -205,7 +205,7 @@
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-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>Clang 14.0.0 (clang-1400.0.29.102)</td></tr><tr><td>Python build</td><td>main, Oct 11 2022 22:27:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Mon Dec 05 22:42:38 2022 JST</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>Clang 14.0.0 (clang-1400.0.29.102)</td></tr><tr><td>Python build</td><td>main, Oct 11 2022 22:27:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Tue Dec 06 21:47:01 2022 JST</td></tr></table>"
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docs/tutorials/05_admm_optimizer.ipynb

@@ -109,7 +109,7 @@
     "\n",
     "To solve the QUBO problems we can choose between \n",
     "\n",
-    "- `MinimumEigenOptimizer` using different `MinimumEigensolver`, such as `VQE`, `QAOA` or `NumpyMinimumEigensolver` (classical)\n",
+    "- `MinimumEigenOptimizer` using different `MinimumEigensolver`, such as `SamplingVQE`, `QAOA` or `NumpyMinimumEigensolver` (classical)\n",
     "- `GroverOptimizer`\n",
     "- `CplexOptimizer` (classical, if CPLEX is installed)\n",
     "\n",
@@ -205,7 +205,7 @@
    "source": [
     "## Classical Solution\n",
     "\n",
-    "3-ADMM-H needs a QUBO optimizer to solve the QUBO subproblem, and a continuous optimizer to solve the continuous convex constrained subproblem. We first solve the problem classically: we use the `MinimumEigenOptimizer` with the `NumPyMinimumEigenSolver` as a classical and exact QUBO solver and we use the `CobylaOptimizer` as a continuous convex solver. 3-ADMM-H supports any other suitable solver available in Qiskit. For instance, VQE, QAOA, and GroverOptimizer can be invoked as quantum solvers, as demonstrated later.\n",
+    "3-ADMM-H needs a QUBO optimizer to solve the QUBO subproblem, and a continuous optimizer to solve the continuous convex constrained subproblem. We first solve the problem classically: we use the `MinimumEigenOptimizer` with the `NumPyMinimumEigenSolver` as a classical and exact QUBO solver and we use the `CobylaOptimizer` as a continuous convex solver. 3-ADMM-H supports any other suitable solver available in Qiskit. For instance, `SamplingVQE`, `QAOA`, and `GroverOptimizer` can be invoked as quantum solvers, as demonstrated later.\n",
     "If CPLEX is installed, the `CplexOptimizer` can also be used as both, a QUBO and convex solver."
    ]
   },
@@ -424,7 +424,7 @@
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+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>Clang 14.0.0 (clang-1400.0.29.102)</td></tr><tr><td>Python build</td><td>main, Oct 11 2022 22:27:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Tue Dec 06 21:47:54 2022 JST</td></tr></table>"
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docs/tutorials/07_examples_vehicle_routing.ipynb

@@ -168,7 +168,8 @@
    "source": [
     "## Initialization\n",
     "\n",
-    "First of all we load all the packages that we need. "
+    "First of all we load all the packages that we need.\n",
+    "CPLEX is required for the classical computations. You can install it by `pip install 'qiskit-optimization[cplex]'`. "
    ]
   },
   {
@@ -177,7 +178,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Load the packages that are required\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -188,8 +188,6 @@
     "    print(\"Warning: Cplex not found.\")\n",
     "import math\n",
     "\n",
-    "# Qiskit packages\n",
-    "from qiskit.quantum_info import Pauli\n",
     "from qiskit.utils import algorithm_globals\n",
     "from qiskit.algorithms.minimum_eigensolvers import SamplingVQE\n",
     "from qiskit.algorithms.optimizers import SPSA\n",
@@ -488,7 +486,7 @@
     "\n",
     "- `binary_representation` : encodes the problem $(M)$ into a QP terms (that's basically linear algebra);\n",
     "- `construct_problem` : constructs a QUBO optimization problem as an instance of `QuadraticProgram`;\n",
-    "- `solve_problem`: solves the problem $(M)$ constructed at the previous step via `MinimunEigenOptimizer` by using VQE with default parameters;"
+    "- `solve_problem`: solves the problem $(M)$ constructed at the previous step via `MinimunEigenOptimizer` by using `SamplingVQE` with default parameters;"
    ]
   },
   {
@@ -771,7 +769,7 @@
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+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>Clang 14.0.0 (clang-1400.0.29.102)</td></tr><tr><td>Python build</td><td>main, Oct 11 2022 22:27:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Tue Dec 06 21:53:30 2022 JST</td></tr></table>"
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docs/tutorials/08_cvar_optimization.ipynb

@@ -13,7 +13,7 @@
    "source": [
     "## Introduction\n",
     "\n",
-    "This notebook shows how to use the Conditional Value at Risk (CVaR) objective function introduced in [1] within the variational quantum optimization algorithms provided by Qiskit. Particularly, it is shown how to setup the `MinimumEigenOptimizer` using `SamploingVQE` accordingly. \n",
+    "This notebook shows how to use the Conditional Value at Risk (CVaR) objective function introduced in [1] within the variational quantum optimization algorithms provided by Qiskit. Particularly, it is shown how to setup the `MinimumEigenOptimizer` using `SamplingVQE` accordingly. \n",
     "For a given set of shots with corresponding objective values of the considered optimization problem, the CVaR with confidence level $\\alpha \\in [0, 1]$ is defined as the average of the $\\alpha$ best shots.\n",
     "Thus, $\\alpha = 1$ corresponds to the standard expected value, while $\\alpha=0$ corresponds to the minimum of the given shots, and $\\alpha \\in (0, 1)$ is a tradeoff between focusing on better shots, but still applying some averaging to smoothen the optimization landscape.\n",
     "\n",
@@ -153,7 +153,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Minimum Eigen Optimizer using VQE"
+    "## Minimum Eigen Optimizer using SamplingVQE"
    ]
   },
   {
@@ -219,7 +219,7 @@
     "# loop over all given alpha values\n",
     "for alpha in alphas:\n",
     "\n",
-    "    # initialize VQE using CVaR\n",
+    "    # initialize SamplingVQE using CVaR\n",
     "    vqe = SamplingVQE(\n",
     "        sampler=sampler,\n",
     "        ansatz=ansatz,\n",
@@ -228,7 +228,7 @@
     "        callback=lambda i, params, obj, stddev: callback(i, params, obj, stddev, alpha),\n",
     "    )\n",
     "\n",
-    "    # initialize optimization algorithm based on CVaR-VQE\n",
+    "    # initialize optimization algorithm based on CVaR-SamplingVQE\n",
     "    opt_alg = MinimumEigenOptimizer(vqe)\n",
     "\n",
     "    # solve problem\n",
@@ -280,9 +280,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "optimal probabilitiy (alpha = 1.00):  0.0000\n",
-      "optimal probabilitiy (alpha = 0.50):  0.0000\n",
-      "optimal probabilitiy (alpha = 0.25):  0.2895\n"
+      "optimal probability (alpha = 1.00):  0.0000\n",
+      "optimal probability (alpha = 0.50):  0.0000\n",
+      "optimal probability (alpha = 0.25):  0.2895\n"
      ]
     }
    ],
@@ -301,7 +301,7 @@
     "        results[alpha].min_eigen_solver_result.eigenstate.binary_probabilities().values(),\n",
     "        dtype=float,\n",
     "    )\n",
-    "    print(\"optimal probabilitiy (alpha = %.2f):  %.4f\" % (alpha, probabilities[ind][-1:]))"
+    "    print(\"optimal probability (alpha = %.2f):  %.4f\" % (alpha, probabilities[ind][-1:]))"
    ]
   },
   {
@@ -312,7 +312,7 @@
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-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>Clang 14.0.0 (clang-1400.0.29.102)</td></tr><tr><td>Python build</td><td>main, Oct 11 2022 22:27:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Mon Dec 05 22:42:37 2022 JST</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.23.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.5.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.6.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.15</td></tr><tr><td>Python compiler</td><td>Clang 14.0.0 (clang-1400.0.29.102)</td></tr><tr><td>Python build</td><td>main, Oct 11 2022 22:27:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Tue Dec 06 21:47:02 2022 JST</td></tr></table>"
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