Commit 6df16bb2 authored by Abhishek's avatar Abhishek

.

parent e7a055b4
...@@ -626,9 +626,7 @@ ...@@ -626,9 +626,7 @@
"\n", "\n",
"$\\max f(x) = x_3 - 30x_4 +x_1x_2 -x_1x_4-10x_2x_3 +20 x_2x_4 + 20x_3x_4$\n", "$\\max f(x) = x_3 - 30x_4 +x_1x_2 -x_1x_4-10x_2x_3 +20 x_2x_4 + 20x_3x_4$\n",
"\n", "\n",
"$\\min f(x) = -x_3 +30x_4 -x_1x_2 +x_1x_4 +10x_2x_3 -20 x_2x_4 - 20x_3x_4$\n", "$\\min f(x) = -x_3 +30x_4 -x_1x_2 +x_1x_4 +10x_2x_3 -20 x_2x_4 - 20x_3x_4$"
"\n",
"$\\min f(x) = -x_3^2 +30x_4^2 -x_1x_2 +x_1x_4 +10x_2x_3 -20 x_2x_4 - 20x_3x_4$"
] ]
}, },
{ {
......
...@@ -18,7 +18,7 @@ ...@@ -18,7 +18,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 9,
"metadata": { "metadata": {
"nbgrader": { "nbgrader": {
"grade": false, "grade": false,
...@@ -26,7 +26,17 @@ ...@@ -26,7 +26,17 @@
"solution": false "solution": false
} }
}, },
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Available frameworks:\n",
"Qiskit\n",
"D-Wave Ocean\n"
]
}
],
"source": [ "source": [
"%run -i \"assignment_helper_QML.py\"\n", "%run -i \"assignment_helper_QML.py\"\n",
"%matplotlib inline" "%matplotlib inline"
...@@ -110,7 +120,7 @@ ...@@ -110,7 +120,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 10,
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
"jupyter": { "jupyter": {
...@@ -141,7 +151,7 @@ ...@@ -141,7 +151,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 23,
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
"jupyter": { "jupyter": {
...@@ -150,8 +160,8 @@ ...@@ -150,8 +160,8 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"n_wires = 4\n", "n_wires = 5\n",
"graph = [(0, 1), (0, 3), (1, 2), (2, 3)]\n", "graph = [(0, 1), (0, 2), (1, 3), (2, 3), (3, 4), (2,4)]\n",
"\n", "\n",
"# unitary operator U_B with parameter beta\n", "# unitary operator U_B with parameter beta\n",
"def U_B(beta):\n", "def U_B(beta):\n",
...@@ -182,7 +192,7 @@ ...@@ -182,7 +192,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 24,
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
"jupyter": { "jupyter": {
...@@ -208,7 +218,7 @@ ...@@ -208,7 +218,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 25,
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
"jupyter": { "jupyter": {
...@@ -285,7 +295,7 @@ ...@@ -285,7 +295,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 27,
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
"jupyter": { "jupyter": {
...@@ -294,12 +304,12 @@ ...@@ -294,12 +304,12 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"n_layers = 1 # Enter the layes you want\n" "n_layers = 2 # Enter the layes you want\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 28,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -316,9 +326,26 @@ ...@@ -316,9 +326,26 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 29,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Objective after step 5: 3.0016887\n",
"Objective after step 10: 3.7093106\n",
"Objective after step 15: 4.1793664\n",
"Objective after step 20: 4.2244814\n",
"Objective after step 25: 4.2668470\n",
"Objective after step 30: 4.3133263\n",
"Objective after step 35: 4.3683533\n",
"Objective after step 40: 4.4358071\n",
"Objective after step 45: 4.5099778\n",
"Objective after step 50: 4.5690799\n"
]
}
],
"source": [ "source": [
"\n", "\n",
"# initialize optimizer: Adagrad works well empirically\n", "# initialize optimizer: Adagrad works well empirically\n",
...@@ -326,7 +353,7 @@ ...@@ -326,7 +353,7 @@
"\n", "\n",
"# optimize parameters in objective\n", "# optimize parameters in objective\n",
"params = 0.01 * np.random.rand(2, n_layers)\n", "params = 0.01 * np.random.rand(2, n_layers)\n",
"steps = 10\n", "steps = 50\n",
"for i in range(steps):\n", "for i in range(steps):\n",
" params = opt.step(objective, params)\n", " params = opt.step(objective, params)\n",
" if (i + 1) % 5 == 0:\n", " if (i + 1) % 5 == 0:\n",
...@@ -335,9 +362,20 @@ ...@@ -335,9 +362,20 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 30,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimized (gamma, beta) vectors:\n",
"[[ 0.48976933 0.9009111 ]\n",
" [-0.49957082 -0.28484669]]\n",
"Most frequently sampled bit string is: 01101\n"
]
}
],
"source": [ "source": [
"\n", "\n",
"# sample measured bitstrings 100 times\n", "# sample measured bitstrings 100 times\n",
...@@ -365,14 +403,27 @@ ...@@ -365,14 +403,27 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 31,
"metadata": { "metadata": {
"collapsed": false, "collapsed": false,
"jupyter": { "jupyter": {
"outputs_hidden": false "outputs_hidden": false
} }
}, },
"outputs": [], "outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [ "source": [
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"\n", "\n",
......
This diff is collapsed.
3.999999999999999112e-01 7.500000000000000000e-01 1.999999999999999556e-01 5.000000000000000278e-02 -1.000000000000000000e+00
3.000000000000002665e-01 5.000000000000000000e-01 1.999999999999999556e-01 5.000000000000000278e-02 -1.000000000000000000e+00
2.000000000000001776e-01 6.000000000000000888e-01 1.500000000000000222e-01 5.000000000000000278e-02 -1.000000000000000000e+00
1.499999999999999112e-01 5.500000000000000444e-01 2.500000000000000000e-01 5.000000000000000278e-02 -1.000000000000000000e+00
3.500000000000000888e-01 8.000000000000000444e-01 1.999999999999999556e-01 5.000000000000000278e-02 -1.000000000000000000e+00
5.500000000000002665e-01 9.499999999999999556e-01 3.499999999999999778e-01 1.500000000000000222e-01 -1.000000000000000000e+00
1.499999999999999112e-01 6.999999999999999556e-01 1.999999999999999556e-01 9.999999999999999167e-02 -1.000000000000000000e+00
3.500000000000000888e-01 6.999999999999999556e-01 2.500000000000000000e-01 5.000000000000000278e-02 -1.000000000000000000e+00
5.000000000000026645e-02 4.499999999999999556e-01 1.999999999999999556e-01 5.000000000000000278e-02 -1.000000000000000000e+00
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