diff --git a/docs/tutorials/03_quantum_kernel.ipynb b/docs/tutorials/03_quantum_kernel.ipynb index cf220c383..e1401b31e 100644 --- a/docs/tutorials/03_quantum_kernel.ipynb +++ b/docs/tutorials/03_quantum_kernel.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -30,10 +30,12 @@ "from sklearn.metrics import normalized_mutual_info_score\n", "\n", "from qiskit import BasicAer\n", + "from qiskit.algorithms.state_fidelities import ComputeUncompute\n", "from qiskit.circuit.library import ZZFeatureMap\n", - "from qiskit.utils import QuantumInstance, algorithm_globals\n", + "from qiskit.primitives import Sampler\n", + "from qiskit.utils import algorithm_globals\n", "from qiskit_machine_learning.algorithms import QSVC\n", - "from qiskit_machine_learning.kernels import QuantumKernel\n", + "from qiskit_machine_learning.kernels import FidelityQuantumKernel\n", "from qiskit_machine_learning.datasets import ad_hoc_data\n", "\n", "seed = 12345\n", @@ -60,7 +62,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -137,7 +139,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "With our training and testing datasets ready, we set up the `QuantumKernel` class to calculate a kernel matrix using the [ZZFeatureMap](https://qiskit.org/documentation/stubs/qiskit.circuit.library.ZZFeatureMap.html), and the `BasicAer` `qasm_simulator` using 1024 shots." + "With our training and testing datasets ready, we set up the `FidelityQuantumKernel` class to calculate a kernel matrix using the [ZZFeatureMap](https://qiskit.org/documentation/stubs/qiskit.circuit.library.ZZFeatureMap.html). We use the reference implementation of the `Sampler` primitive and the `ComputeUncompute` fidelity that computes overlaps between states. These are the default values and if you don't pass a `Sampler` or `Fidelity` instance, the same objects will be created automatically for you." ] }, { @@ -147,19 +149,16 @@ "outputs": [], "source": [ "adhoc_feature_map = ZZFeatureMap(feature_dimension=adhoc_dimension, reps=2, entanglement=\"linear\")\n", - "\n", - "adhoc_backend = QuantumInstance(\n", - " BasicAer.get_backend(\"qasm_simulator\"), shots=1024, seed_simulator=seed, seed_transpiler=seed\n", - ")\n", - "\n", - "adhoc_kernel = QuantumKernel(feature_map=adhoc_feature_map, quantum_instance=adhoc_backend)" + "sampler = Sampler()\n", + "fidelity = ComputeUncompute(sampler=sampler)\n", + "adhoc_kernel = FidelityQuantumKernel(fidelity=fidelity, feature_map=adhoc_feature_map)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The `scikit-learn` `svc` algorithm allows us to define a [custom kernel](https://scikit-learn.org/stable/modules/svm.html#custom-kernels) in two ways: by providing the kernel as a callable function or by precomputing the kernel matrix. We can do either of these using the `QuantumKernel` class in `qiskit`.\n", + "The `scikit-learn` `SVC` algorithm allows us to define a [custom kernel](https://scikit-learn.org/stable/modules/svm.html#custom-kernels) in two ways: by providing the kernel as a callable function or by precomputing the kernel matrix. We can do either of these using the `FidelityQuantumKernel` class in `qiskit`.\n", "\n", "The following code gives the kernel as a callable function:" ] @@ -199,7 +198,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -241,7 +240,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`qiskit` also contains the `qsvc` class that extends the `sklearn svc` class, that can be used as follows:" + "Qiskit Machine Learning also contains the `QSVC` class that extends the `SVC` class from scikit-learn, that can be used as follows:" ] }, { @@ -283,7 +282,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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noEPpik/Ik5Vt3K4m5CnOyLp1Ggct8brhhh5cc81o5ncQ11wzihtu6JnMZtM15La2tvmf/OQn337nnXe+mEoVlidoVvqJ2RYuXGg7d+4cc1tQaWK8YkoV+Q516uunI+gtksDQ0PFQrxU0O9qqof05X7cUCunMS5It9fMm7M/MDtJi31cx34/sJS13Ts+PqnO2lN+fUsX4IA7iiA0XfCruU0899eKll156NPQT7rqrBbfc0ornn5+C888/gRtu6JlMh14hnnrqqdmXXnrpeUH3qZkXQnrcLtAE+FNNr4ozoIQL2p9xSZe0xiN1en5F++xne8uVgAuhkoWIiCMibyHnK1VEUabIfN3m5k2BrZ3MscxhuVAicCGGJKmEU41dH1supVPxJQtX6oAiEqnR0dFR1tTUOL1a0ejoKIHsS0WpZCEileDpI0eONPkJz0mjo6M8cuRIE4Cnsz0mkhbyk937c5Yqij3xo5BDzsme+urC4a0LMVS65mYrWUmrHFwtX8Qd18jIyGcOHTp096FDhy6Guw3NUQBPj4yMfCbbAyq+ZCGSi0palWHRokWvAFgedxyTVbaEHKbFGvsplRKr9HjvaQ0NZR3vnVRBvw0Xxq4Xcvr3+DH+PxoobO6HSuNq016qTHo17FkPPwQOD2PWww9h9ZyUpuasYEGf+awqP2ZXQhYnaDXs6hP0mfPtb487rFhF8v/RZW3zsHNnuE41lSkE0GrYpeLSbydfR1/gZ97YGHVYTlMLWZyg1bCrT+BnPhDzufQxU0KWWKWXc2oZ7Id1dGgWuCoSNJOivfBC3GHFKpYSugsTakv80p066eWccNNNsI9/HDh27MwoixiWdZLSCypfjFkB3R9l8WqVL8RT5X2aEievU6fT69QBgK9+FXz/+/Hq8qvw9wPeAgCaVa+yjZ/5bxBDcYcUK5UsJDbZOvK85bFEqk/ZWsgqU0ha+jO3xYvBrq4zLWTgzPJYJerb0ffLPcWc0FIt1EKW2GRbzknLY0m1inwJJ40zllyClnPS6dLVa+nSpXjiiSecnbEtamqKSKy0PJbIGSpZiIg4IpIW8oG9+yaUKkqxkrBKHZVHn6nIGWohi4g4QglZRMQRoUoWJF8E8DqAUwBGzGxxFMFoRIYb0stvTXYZLKlsk11RXiYqpIa8zMyORhaJiEiVi6RTb+6Ci3CrPw65EGoNl1e+Fo5INq2t03Cy754Jt6emHAfn63tVrLA1ZAPwY5J7SF4bZUAi4r6+PsIMEy4jJ6bHHVqihW0ht5tZD8k3A/gJyW4zezTzAX6ivhYA5s6dW+IwRUQqX6iEbGY9/r+vkNwG4J0AHh33mI0ANgLeqdMljjNS6c7EaiiZqEwhUVNnXvHylixINpCckf4bwAcBPB11YCIi1SZMC3kOgG0k04/fYmY/ijQqEZEqlDchm9kLAC4tQyyR05yrZ9gzdwR2wKiXXMJobjb4jbQJt0vxNNtblRo5MR1BM6+S01FX/nAkYXp63og7hIqkU6dFRBxRsS3kQsoTGl0hIi5QC1lExBGJbyEX21FXDa1iEUkWtZCrVGrKcZCYcElNOR53aCJVK/EtZCkO51+n0RQijnE+IZdy7HC1lSnSHXnemOPwM3Pp1FeReDifkGXyNOZYJBlUQxYRcUTsLeSoT2eutjKFC7QUl0hx1EIWEXFE5C3kck7oo1ZYfGfk5fucM+/X5yQSTC3kKqAxxyLJEHsNWaKnMcciyRBJQj6wd1+kpQod8pZeMWOPi/2MVb4QCaaShYiII5SQRUQc4XwNWYe0IlIt1EIWEXFEJAn5XJzAzY39WILBKDYvIlKRoilZLFyIWd/8JlZ3rAIOD2IXGgp6usoUbivlCJr0tvSZFy/byUCTnbVvsp+zPtPCRVOyIIFlyzB16xasaByJ5CVERCpNtJ167e1oGewH0BTpy1S7Yk+X1rzHlSHzc8z8LqT/LvZzzmzhFtNazvYctZyzi7ZTr6sLvQ1KxiIiYUSTkM2AHTsw3LEK2wecH1knIuIEWtBSEpO0mLRHGmeeTsYrGkfQMtiP3oYmbB9IZe3kq4ZDmSjmCi53yUJzWE9eOQ7n830v4u70C/IgDuKIDbPkG06ISFrIv8MU/P2AV6pYPSeFWQ8/BA4PY9bDD2H1nJSGw4mIBIi0hryicQRTt24Bli0D6uo08kJEJIdIC7wtg/1Ae/vYG8eNvKjUw9NyTsxfiCSMrKjm2eBKWdJKf9bZShdBtxfy/cgWl6vf/SSItIXc29AEdHWNvVEjL0REAkXSqXcWp9oncDaWYBCr56S8skV7O9DVBVuzBifXr8eplStL/rouKKZ1kLRWYNwtoGL3l6st72L3Z9SdwVEdTeV6v9XeqRdpyWIXGoDDg1ix/Cq0DB2DtbVhpIKTsYjIZEQ+SHgXGrBrALh16OWoX0pEJNFClyxI1gLYDaDHzD6a67ELFy60nTt3liA8d5XysN2lw+dCxF26yGUJBnOOf3d1n8dd8gozpj3KjuGlS5fiiSeeqNqSRSGdep8D4Oa3WCRDuu9C498laUIlZJLnAPgIgLujDUdk8jT+XZIqbA35WwDWAZgRXSjuK/YQPd/hc1JNdjawqIQZ/+6q9D4tZH+W8jTsJIxTr2R5W8gkPwrgFTPbk+dx15LcTXL30aNHSxZg0unwufw0/l2SKkzJYimA5SRfBPAAgPeRvH/8g8xso5ktNrPFs2fPLnGYyaXD5/LbPpDCcMcqYMcO4ORJzTwoiVHQiSEkLwfwt9UwyqJUh+B3cj84POwl47STJ2FTp+KvbB4Ad3v8SyGuUkZQmWjV0P5YYimlSl9WqdpHWajJELHehibM6uryWshp6cPngfjiqnTp8e/Amf28Ks6AREIoKCGb2U8B/DSSSBwQRWtu+0AKqztWjTl9vJoOn4vppIri9StF0PsptgOw0vZNJaiOrBCjMaePV9goCxEpLSXkMgg6fBYRGU8J2QHVsDpvOefOraT9Fkax+zaK5cRkcqJddVpEREJTQhYRcYRKFnDrtN9M1XBIOdlRGK7uj3KvBB6kmBEZGoURL7WQRUQcoYQsIuIIlSwSplIPKSvpvbhM+9ltaiGLiDhCLeQES7eW1epxQ7EdeSJpaiGLiDhCCVlExBEqWSD/Ib+r45RVqnBDvlKFlkWSsNRCFhFxhBKyiIgjVLIIoZSlgUpfgqcShR09odKETJZayCIijlALuczUwk2G8a1ie+YOjJyYDgBIpYCRgEXDW1sNPT1vlCM8qVBKyCIhjJyYjvQC7SQQtFg7WbWLJUuJqGQhIuIItZBFfEGdd5mliswGcEsL0NtbrsikWighi+SQWarIpOqEREEJWWSczFaxSDkpIYv40uOI6+vHduBlM/6+5uaAprRIAZSQRYo0NHQ87hCkwmiUhYiII9RCFsmhuTm4bKHyhERBCVkkh8yhbaTKFBItJWSRcZqbLfCsO7WKJWpKyCLjaD4KiYs69UREHKGELCLiCCVkERFH5E3IJKeR/CXJp0juI3lTOQITEak2YTr1hgG8z8wGSNYB6CL5r2b2WMSxTRB2KZ1CaekdEXFB3oRsZgZgwL9a5180/kdEpMRCDXsjWQtgD4DzAdxhZo9HGdSbZiJwtq3UlDvA+YW1krPN3JWacrzgbYmIRClUp56ZnTKzywCcA+CdJC8e/xiS15LcTXL30aNHJxVUeg7a8ZdipkQs5bZERKJU0CgLM3sNwA4AHwq4b6OZLTazxbNnzy5ReCIi1SNvyYLkWQBOmtlrJKcDuALAN6II5kyn3T05H5e0MsT19RcW9TytUC1SXcLUkM8G8G2/jlwDoNPMfhBtWLllX1ZnOurKH46ISEmEGWXxKwALyhCLiEhVc2pyofR44NbW4Nm2UlMKn/owNeU4vErLWM3Nhp4Ixx8XW6bItw2VMUQql1MJOS1ztq3JngzC+dcFljGiTMYiIsXQXBYiIo5wsoWcKei05mwljajLEC7ILGOofCFSWZxPyEE0gbiIVCKVLBLs+voLS9J5KCJuUEIWEXGEErKIiCMSWUNOgswON5UVyrMP1MkpSacWsoiII9RClrJYgkGsaBxBy2A/ehuasH0ghV1oiDssEackPiGX4lA46kPdSi1fhH0vSzCI1XNSmLq1E2hvx6yuLqzuWAUcHlRSFsmgkoVEbkXjCKZu3QIsWwbU1QHLlmHq1i1Y0TgSd2giTkl8Czlb67aQlmjYx5aiJR20jSS0micTY8tgP9DePvbG9nbvdjRNLjAZIwlzb+eK8QAOli0OF6mFLJHrbWgCurrG3tjV5d0uIqcpIUvktg+kMNyxCtixAzh5EtixA8Mdq7B9IPEHaCIlRQtaemOSaupnW+qC5QU/L2gioVKKqjRQqeNfS7m/ohplUan7PpskfYeLifVBHMQRG544c1iVUBNFymIXGrBrAACagIG4oxFxk0oWIiKOiKRkQS42YPeY20q5InRUpY0kjGkuhySM+kirhP0dpBJONVfJonCRtJAXLQLMxl5GTkxc105ERM5QyUJExBGJ7NTLtvDpZEsZlXr4W2kq7XMqZ4moHPsuSSUv16iFLCLiiLK2kEvVGZethZx5e9RjmqX0gsYqrxraH3dYieVip53kFkkLec8egBx7aW4u/WgOqRzpGeFmPfwQODyMWQ8/hNVzUqjt7Iw7NJGyiSQhL1gwiqGh42MuWilacsk2I1xqw4a4QxMpm0R26qkcUXmyzQjH7u54AkqAuDo3VaqIjjr1xAnZZoSztrZ4AhKJgRKyOCFoRjhbswYj69bFHZpI2URSsjiwd9+Ew5pKGzsqpbVqaD/Y2YnRtWvB7m5YWxtG1q/HqZUr4w4tcvptSFoia8hSmU6tXFkVCVgkGyVkKZhWkBaJRtkScraeWR2uJUspVpDWZ159wn7mv1y6NOJI3Ja3U4/kXJI7SD5Dch/Jz5UjMHGTVpAWiU6YFvIIgL8xsydIzgCwh+RPzOyZYl90si2kMOMg1QqLhlaQFv22opO3hWxmB83sCf/v1wE8C6A16sDETVpBWiQ6BY1DJnkegAUAHo8kGnGeVpAWiU7oXxHJRgAPAvi8mR0LuP9aANcCQCNqSxZgkDCHTEFlDR1qhZPeT0H7cBcagMODWLH8qoJGWWjfi+QXKiGTrIOXjL9rZg8FPcbMNgLYCABncaqmdqtgWkFaJBphRlkQwCYAz5rZLdGHJCJSncK0kJcC+BSAX5N80r/tRjP7YWRRlYAOkd2gz0EkvLwJ2cy6AFTtstwiIuUSSdf43AUX4dadO6PYtJRRttZtvnHgahWLFEfTb4qIOEIJWUTEERrNLwVTSUIkGmohi4g4QglZRMQRSsgiIo5QQhYRcYQSsoiIIzTKQhJHJ6ZIpVILWUTEEWohS2KEWbpLotf0h9cBAPp/fkfMkVQetZBFRByhhCwi4giVLMRptZ2dSG3YAHZ34+bGN4VaLkqKly5HZLJn7sDIiekZt9wDAGhtNfT0vFGmyKqDWsjirNrOTtTddBNqbr8dfOMNzHr4Iayek8ISDMYdWlUZOTEdZphw6evTNOmlpoQszkpt2ABu2gQsWwbU1QHLlmHq1i1Y0TgSd2gikVDJQpzF7m6gvX3sje3taBnsB9AUS0yVJKg8IfFSC1mcZW1tQFfX2Bu7utDboGQslSmSFvKBvftOjxldgkGsaBxBy2A/ehuacnbK6AyrsfK1YCp1HGjmd2d1xypM3brFayl3dWG4YxW2D+jArhBqCSdHpC3kJRjE6jkpzHr4IXB4WJ0yUpBdaMD9h0fw6vKrYFOn4tXlV+H+wyMaZVFmqSnHQWLCpbnZ4g6t4kTa1FjROIKpWzu9ThngTKfM8quwayDKV5ZKsQsN/nelCdB3Jhacfx2GKvRozDWRJuSWwf6COmXSh6oqXYST1FNYCzkFupCSl4w1vlQxcTyxJzXlODh/Ylkjad+rShBpyaK3oUmdMlI0lbxKK9t44qAkLfGINCFvH0hhuGMVsGMHcPIksGOHOmUkNK/ktUXjkKVqRJoZd6EBODyIFcuvKuiQM/OQtprLF+lDxmrtJS+05CWTpzJFvCJvqqpTRorV29CEWV1dZzqFgTMlL32XpALpxBBxlkpeUm2c/2arfJFfZknD1UPOYiaXL7bkVc1ylbe88cQTO/Camw09jn5vqo3zCVmqm0pepcP516Eu47qr/3lXs0Ql5Godp5z5w6nWDj4pDVeTcPq3fQAHY44kXqohi4g4QglZRMQRkZQs5i64CLfu3AkgmpWCq7mjrxrKFzpduniuliQybamfN+HzhT5fAGohi2N0unRlq+3s1OebQ96ETPIekq+QfLocAUl10+nSlS21YYM+3xzCtJDvBfChYl/g1qFnq66sUC79P78jEYeohch9urQk1fX1F+L6+gvBZ5/V55tD3oRsZo8C6C1DLCKaIbDC6fPNrWzjkDNbyVF09FWKYjosK6mVvH0glXXZpkrq7Mv2G4j6aLKcrxv0Wtk+3xm3fwu3rlyJXy5dWvI4kqRknXokryW5m+Tuo0ePlmqzUmWyLdsEQJ1BFSDb53tq5cq4Q3NCyVrIZrYRwEYAWLhwoRbbkqIFnS59c2O/lgOrEEGf76o4A3JILKdOT7Z8US2dhEH7plree1r6/U5rbAzuDBo6hluHXo4hsmiUcnqAfL+tqL5Lcb1uJQgz7G0rgF8AuIDkSyTXRB+WyFjW1hbYGWRtbfEEJBKBMKMsOszsbDOrM7NzzGxTOQITyTSybh1szZoxcyPbmjUYWbcu7tBESib22d6KKV8k4dTpJMxR7KqgzzTd6ZNauxbs7oa1tWFk/frEdQZFPcLIpTKFq79Nl8WekEXCOrVyZeISsEghlJBLqLV1Gvr66F+7J+N2Q0/PG6G2UUljbUWkMErIKN1hXl8fYQED/kieLmHkKl/Udnbi02+bAW7aBLS3Y1ZXF1Z3rAIOD55Oykko1xSjkt5LKYT5nMOWP8qxb/X5lYZme3NIasMGLxlr4hWRquRUCzn9v6xrp1ZPZmxoS4v378knvRJGfb13vbl5YhmD3d05Jl7Ruf7Vqtjfg1qtyaMWcsT6+gCziZczteYzso211cQrItVBCdkhQWNt0xPriEjlc/KX7uqhVr6OluZmAzmx5ZvN+I6+oLG2/MbXsevq9TnjcXV/ZUpCjKVU29np9Ql0d2NgynQglULj4DHc3BjtyBmX9rPG4hdOLeQS6ul5A0NDx8dcCnVq5UoM796NNwYGMLx7t8bdJlBtZyfqbroJNbffDt57L2a8eRZmPLxNs9RJXk62kEWSbMxomYsvBu69t2pmqSvFWPxqpoSMaEd3ZCtjpKYU3nqWZBgzWibnkkWl6ax1oUyRLk+c7Lsn61h8yU8JOWLjWwWZdTWpTNbWBnZ1ea3iCy/0Rs6kW8jAmZEzFdhClslRDTlhlmAQNzf2407ux82N/apFOmjMaJkvfhG4+mqNnJFQ9K0oUrGnMKd7mwtpKae3X9vZCfvil7yVMzJOrWZnpzr/HDJ+tMzAlOnA8o+jcfDYpOcncaE8kaajvdJTQk6Q1IYNqNm6ZUIH0ejatUrIjsmcme4Lp//znqkyheSkhJyhnCtjFzMuM9up1ezuLlFUEreg2f5WDe2PO6wxcrWMU1OOg5w+4fbmZi2zGYZqyAmiZYwq2xIMBq6sXdvZGXdooXH+dai77NOou+zTY8bja8hbOGohJ8jIunWoW7Pm9PSc6OrCcMcq8Btfjzs0KYEVjSOBK2u7VJJqbZ2Gk333TLg9NeU4OF815clSQs6ikPJFueYoznZqdSE/Vp3O6q6WwX7nS1LZ5/yejjr/b32viqeEnDBaxqhy9TY0YVbAmGWVpKqHasgi47S2TkN9/fQJl9bWaZG+7vaBlFbWrnJqIYfg6sT5xdDhZH65luKKQmaZ62Rnp5Mra58pdU2sH6fpuzV5SsgiDlFJqrpVVUKebIdWOccpS2Vz6Yy7IGNnbQPSLeNUKniyLI0zLo2qSsgiEk6usk3dZZ8GoBJFFNSpJyLiiIpvIWc7zbOU5QupDOnvRCq1KfCwvK6uuMPySvuuqGUcnYpPyCKFGhkp7ygLkTQlZKlq2ZYcEomDEjImrv4s1SOo80oN4exLj2k0RbSUkEVkAs3OFg+NshARcUTFt5AzyxBackbScp0K3NwcXLbQ4bpELVQLmeSHSD5H8nmSX4o6KJE49fZ6/46faF2H8RK1vC1kkrUA7gBwBYCXAOwi+bCZPRN1cKWWtNZy+vTsShvH6pJsSw6lphyPIZpkyTZ9gL6vxQtTsngngOfN7AUAIPkAgCsBJC4hi4zH+dednlhdJG5hShatAA5kXH/Jv01EREqoZJ16JK8FcK1/dbi+vv7pUm07YrMBHAWA+vrNMYcS7K76+vSfp2NNgCTFCuSI18HvhdP7NuP7ChQe67mljSZZwiTkHgBzM66f4982hpltBLARAEjuNrPFJYkwYoo1GkmKFUhWvIq1coUpWewC8A6SbyM5BcAnATwcbVgiItUnbwvZzEZIrgXwCIBaAPeY2b7IIxMRqTKhashm9kMAPyxguxuLCycWijUaSYoVSFa8irVC0YLmGRQRkbLTXBYiIo4oaUJO0inWJO8h+QpJ54fnkZxLcgfJZ0juI/m5uGPKhuQ0kr8k+ZQf601xx5QPyVqSe0n+IO5YciH5Islfk3yS5O6448mH5EyS3yfZTfJZku+JOybXlaxk4Z9i/W/IOMUaQIerp1iTfC+AAQDfMbOL444nF5JnAzjbzJ4gOQPAHgArXNy39CbRbTCzAZJ1ALoAfM7MHos5tKxI3gBgMYA3mdlH444nG5IvAlhsZs6OQc5E8tsAfmZmd/sjtOrN7LWYw3JaKVvIp0+xNrMTANKnWDvJzB4F0Bt3HGGY2UEze8L/+3UAz8LRsyXNM+BfrfMvznZUkDwHwEcA3B13LJWEZBOA9wLYBABmdkLJOL9SJmSdYl0GJM8DsADA4zGHkpVfAngSwCsAfmJmzsYK4FsA1gEYjTmOMAzAj0nu8c+MddnbABwBsNkvB91NsiHuoFynTr0EIdkI4EEAnzezY3HHk42ZnTKzy+Cd1flOkk6WhEh+FMArZrYn7lhCajezhQA+DOA6v+zmqhSAhQDuNLMFAAYBON2v5IJSJuRQp1hLcfx67IMAvmtmD8UdTxj+IeoOAB+KOZRslgJY7tdmHwDwPpL3xxtSdmbW4//7CoBt8MqErnoJwEsZR0ffh5egJYdSJmSdYh0Rv6NsE4BnzeyWuOPJheRZJGf6f0+H18nbHWtQWZjZ35nZOWZ2Hrzv6/81s9UxhxWIZIPfoQv/0P+DAJwdIWRmhwAcIHmBf9P7oSl78yrZbG9JO8Wa5FYAlwOYTfIlAOvNbFO8UWW1FMCnAPzar80CwI3+GZSuORvAt/1RNzUAOs3M6eFkCTEHwDZ/JegUgC1m9qN4Q8rrrwF812+gvQDgmpjjcZ7O1BMRcYQ69UREHKGELCLiCCVkERFHKCGLiDhCCVlExBFKyCIijlBCFhFxhBKyiIgj/j847+vGxCcs0AAAAABJRU5ErkJggg==\n", 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" ] @@ -343,7 +342,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We again set up the `QuantumKernel` class to calculate a kernel matrix using the [ZZFeatureMap](https://qiskit.org/documentation/stubs/qiskit.circuit.library.ZZFeatureMap.html), and the BasicAer `qasm_simulator` using 1024 shots." + "We again set up the `FidelityQuantumKernel` class to calculate a kernel matrix using the [ZZFeatureMap](https://qiskit.org/documentation/stubs/qiskit.circuit.library.ZZFeatureMap.html), and the default values this time." ] }, { @@ -354,18 +353,14 @@ "source": [ "adhoc_feature_map = ZZFeatureMap(feature_dimension=adhoc_dimension, reps=2, entanglement=\"linear\")\n", "\n", - "adhoc_backend = QuantumInstance(\n", - " BasicAer.get_backend(\"qasm_simulator\"), shots=1024, seed_simulator=seed, seed_transpiler=seed\n", - ")\n", - "\n", - "adhoc_kernel = QuantumKernel(feature_map=adhoc_feature_map, quantum_instance=adhoc_backend)" + "adhoc_kernel = FidelityQuantumKernel(feature_map=adhoc_feature_map)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The scikit-learn spectral clustering algorithm allows us to define a [custom kernel] in two ways: by providing the kernel as a callable function or by precomputing the kernel matrix. Using the QuantumKernel class in qiskit, we can only use the latter.\n", + "The scikit-learn spectral clustering algorithm allows us to define a custom kernel in two ways: by providing the kernel as a callable function or by precomputing the kernel matrix. Using the `FidelityQuantumKernel` class in Qiskit Machine Learning, we can only use the latter.\n", "\n", "The following code precomputes and plots the kernel matrices before providing it to the scikit-learn spectral clustering algorithm, and scoring the labels using normalized mutual information, since we a priori know the class labels." ] @@ -377,7 +372,7 @@ "outputs": [ { "data": { - "image/png": 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zoh68AfBsKCuj8WVNWXlO/3Rewsa20SOpJ7Vlgz582VgGG5biu25ZE0efbQPJcKbF86lTvnIzht2JGYYRaMyJGYYRaMyJGYYRaMyJGYYRaDo0sO/qm9Rkbt9UopL5TyrNV0rEgvjlC3i5C/rp0odFhTyImN5DBx1P6XOs0ron88DvL//8nNLiY/h05AwyZXruB7rkBwBQoIO059x4odJ8vZpqKnU50rZVOtgPAMOmjlAam4wOAMtX5SttwrFDqS0PPut+U77JOazkpk9qf2r71pa5SjsmcyS13VOrk0QZIZ5QKq/XpV6r9uiyoROy+VShMJkgnkKmcQG8TMv3/taRPmWs99iuaj7FKSdFT233JSdE2p/IYNOdfCVdPp1hd2KGYQQac2KGYQQac2KGYQQac2KGYQQac2KGYQSajm2K2NAEFLdsIJjmycawqUSsoSHAS4lYFhIAsImU8nhsGxp0Rmh+gc4YntRrAn+tSp1Zi4vhDfJCsTrDGh/itvWddIbzvbW6DCdmvKfchZQo1XcjU6AAbN6qM1hJCbycqX6HPreb8z0lWbHk9cjkLTZRCADGdRuvX5+UywBATqrOHvvKWrISdekTmzQEAE0k49c5UWcyG5t46RVIZs+3X00kO1npKSXKDOljYFnPJNKEEgAcOd6aRp1ZBABhTU09t0aszKquiZf8+aY+MexOzDCMQGNOzDCMQGNOzDCMQGNOzDCMQNOxgf3EWGBwRgspPeQJ4NXpQKZvKhHrB+YrJaJBfBbsB5A6SQdIWZnUiM55/LVq9THE7sffjfqVvBSIkZ6u+37FeCZJ9crWk2Q2blxHbU8770Sl7QjrchsAKNoVVtrgQXyiTgw5D5WN+n3wlZ8sKJqvtOGddYkUAKzbs1lpp/fWSQQA2FmtExF1jbqMBwDWhtcqbU9tWGnppMQKABz0PuSk8KlTbGJScqKeSgTw8p6qen1uC6t4oowlLEKxPJmTGWr/VKLdNUVKS47nx1DRwK8xht2JGYYRaMyJGYYRaMyJGYYRaMyJGYYRaMyJGYYRaDp22pGIaqh3z/xnqG36gPZnPdhUItbQEOClRCwLCQBFH+UrLe0bOpvy+Mq/0/X9Jw1SWrWnfGNh8adK6z6xL7WtrNZNDcvKdAlKHZnyAwAbF21SWubwbG5boM/t4iU8k4md+tgWLF5DTcvP0Nljlk3tl67PIQCsJ00Rk+N0hhYActJ02VFCDJ+GlUkaIGaSKUwAMKbrGKUt3b1Eab7ynoRYXT6WFp9BbRlsKhHAp0at2vOZ0oZkDqfr6xr19eWDldGxBowAUEO2m+LJTtbuxz7YnZhhGIHGnJhhGIHGnJhhGIHGnJhhGIFGHOnhdKjIGZrjrvv7tS20Kb35mPoNpRuV9sAHr1DbMX11qQabSgTwfmC+iUtpIR10POfq7ylt1XOv0vWzN+r9PSGH9x5j0442lOngNQDUNOig511zXlDa/WddT9dX1uupQqV1vMzjgXn6GLIzedJkUq4Owj+76GNqO/vi3yktOVYH5leXrqDrR2TpoHpNYzWxBBqd7ue1i5TAAEBJjZ5AlOoJPq8oWam0aX3OUdqyksV0/diuE5UWrtPTlgAexJ9b8D61PS33DKWxAPyGMp50YVOjqht577LkOH1uGjz901jipoiUeQFAbmpfpXUKZS50zo1V26VbMAzDCAjmxAzDCDTmxAzDCDRtOjER+auIFInI8mZaloi8KSLrov/rgI5hGEYH0GZgX0ROBFAB4O/OuWFR7W4AJc6534jIzQAynXM/buvFBg4f4O55+dcttO88/TC1/dfMXylt8S4dlAeArWU6OHhaHz46vrxO91Xy9QNjT+JfOGiG0oZcOJWuX/f8G0r724qnqe0nO7Yq7amp+hwAQFmdftq9tC6stF01vB/ZO1vmKe2yIV+ltg8ufVJpTZ5r5vhc/QT40iIePH5/nU7cvHvZE0rzPZW+s3qH0nJS+lDbcJ0O1vue2GfDLOqbeD8xauv0/ibF8if2WaC71BvY18Fy31Pt7In9ejKQIyWOD8hhfc5ihA+dEehhJyyREtkHdh71egBYUKwTQqf3OufAAvvOufcBtD6z0wHMin49C8CMtrZjGIZxKDjQmFh359zetpCFALofpP0xDMPYL75wYN9FPo96P5OKyEwRWSAiC8pK2t9y1jAMoz0cqBPbKSLZABD9nz85CMA596hzbqxzbmx6VvsHYhqGYbSHA3ViLwO4Ivr1FQBmH5zdMQzD2D/ak518BsBJALoA2AngdgD/AvAcgN4ANgO40DnH0yrNt9U50WFa7xbavHsfo7YTr/+G0nqN7kttExN0ScW65XrCDQCgkmS7yFQigPcDmzllstLOHzidrh90/mlKC7+i+zoBfEx8j2/p1wJAj+HMy05W2j/PeoAuz3vgfKWt/4D3CBs9bZTSEhL55Jv5C1Yp7YRJfALR8+fcp7RGkoFbWKynGgFAz5SeSuuerDUAuHuBPg/fH3UNtd1WqbPEXRK7Utslu3Q5USWZNDSu2zi6PpZk/JLikqgtI45MQAJ4djAzQffn+3jnh3T9sCydZfZNzmJZy1CM7pMGAFUNutyt0fHfvbR4XdqWkdCZZifbbIronLvE86Mpba01DMM41NgT+4ZhBBpzYoZhBBpzYoZhBJoO7Sc2aswo9968OS20X336W2p7y9gfKK3K09OIBUjjPcFF1lcp1uPL2VCPtaU6eP3Kxrfp+h+NuVFpGWfxQPdRU3UwddG3/0ltWeCWlaB0vfpEuj78uC7p8A132ENKdnxB3oyQLnfxldEM+cXFStvyS92XrcnTmyotlKG0qgZ+fTSR4HEhKVsCeC+tynpdqgZ4riXRYeZ1ZfqaAYAhGfo9Z8kNgJczsZIfH6zXWifSw85n6ytxKqnVpW3sHABAerx+xGpzRT61HZE1WmmdErKsn5hhGEce5sQMwwg05sQMwwg05sQMwwg05sQMwwg0bT6xfzBpco0qw3jPc3yC0XfyvqU0loUEgJ3VBUpj04MAIBTLm+ExFhZ/qrR+af2UxhoaAkBNns5usiwkAKx5dZnSyr4VprYsg7W5YpPSTjyLT3zaWLZWaVmJOrMIAJUk4zevkE8wOq6Hnt6zoGgBte3fN1tpdSQDFufJdG2v1GVl5fW8S0onkslMiuXlPUL+rhdUbae2nRN1KU8dafzXMzmHrt9KMnM9k3OpbWndHqXlJPMmkKvD+loKk6aZw7JG0vVztr+ltOGkFAkAmkhWu76JT53aTSZJHZXBJ42tIsfgw+7EDMMINObEDMMINObEDMMINObEDMMINB1adhSbneKSrxjaQnvlhruo7VduvV5pF35V98wCgIIyXRYy9wM+GSk+pEtF6lfyqUDdJ/ZV2mNX3qC0Y7tNoOtZP7DKvy6itmX1YaVln8O3iySd4Djzm7oz0otn/4kuz/uj7ie25mPeT+y7181Q2rIi3sh37oe6V9r0KTy58LfT71ba7hq9XZZYAICFxQuVNqbrGGr75OrnlXbtcN2vDgAKSZIo1TMVaE1YT3IqrtbXUl5XXmpW16iTAIM6HUVtWSkPm7YEAOXkWmIJiy2ekp/U+FSlJXgSYtkkEeHzKFtJ8imLTGYCeElVdkpvKzsyDOPIw5yYYRiBxpyYYRiBxpyYYRiBxpyYYRiBpmPLjuobUVHcMpNY7mk4hyJdurCxsJialpWSDFYBz2rVd+LNEhmV1boMpqZBa2V1pZ4N6EZ2rKEh4GmGR7KQAIBq3eQvOU6/lawkBAAKi3UJC8K11JZlIivKdTkVADTU6Gzb7mpegtJAjjeRlALVN5HpVADS4nXG0Jfpqm/U20jwlh3prBgrWwKAzAStl9Toc+vbrxpWZuXJODJ8jSxDMTqTyNonxpOmjgAv2dufcj1fqVinkJ5g5Gug6Gu8SW3bbWkYhvElxJyYYRiBxpyYYRiBxpyYYRiBpkPLjtIHdHHH3j2thTZjsJ5qAgDLinWJwvZy3i8qXKmDxxP79Ka2763doPcrPYXalpXp5EBDgw6qPzpDT2YCgJ99+LjS/n7Gr6ktm6J057z/pbYsiP/CA3pSUOHLvO/Xz+bdq7Qmz3WwYY8OVM//dCW17ZKlA7dbC3gypvSeuUpjI+3rSX8ugAfgN5TrPmkA0C91gNJWhpdT24Hpg6nOYGU7w7LylMb6twFAToq+RllyI4I+XlZeBAAhMukrNkZfM9srt9D1dY06ybMmzI/hrD7T9XrPe8YKkhJjk6klS4ClhzKt7MgwjCMPc2KGYQQac2KGYQQac2KGYQSaDg3sjx4z2n3wyfsttJfzX6S2Z/eZobSaJj5KPUSecvaNeI8hw0Z8TwezAOeS3bqPVZWn59UJ2bqfWOo3eM8rNtTj9QsepbbsSXwW5O0xnfcjq3pV98FqIkF1AKhu1E/n+54UT47TfagqGngy5pJXblXa7Ol/UFpVQwVdn0J6fFV4BoWkxuuEw4LiedSWDc/wbTeFHG9KfLrSXtn8L7p+au9zlMbONwBUNujKFl81A/t9YNdMGjkvAD/nu2p4gmZ3bYnSasnvDQAM7qSTJpWe97df2iClZSR0tsC+YRhHHubEDMMINObEDMMINObEDMMINObEDMMINB3aT2zxllVIuXZcC+36S8+ktl//031K69+7B7XdWqAnzNRU8kxmfKLO3PTK1uPoAWDjIl369OpvdcnOu1t5ec93nntQaeHHuS0rTWFTiQDeD+yiKToTybKQAJB8pp6okzKan9uMdJ2BC5fxjFJlic7S9uqfTW0/ueEJpbFsW1IsLwl7e/vrSjsl5wxq+8Km55Q2ve951LakVmfhuibyY6gl2fJFuz5R2uTc0+h6lon0leGwrHoVyVgCQFZCN6XtqNIlRrFkmwCQQfqfZSV0pbYQ8hSA54kHlqnulsTPbVkd6Xnnwe7EDMMINObEDMMINObEDMMINObEDMMINB0a2IdzQEPL8ocpfcZT0wdX/VNpoUG9qG2XTF3qsW2VDvYDQH033a9p48Z11DZzuA46ltbp4ORlQ75K19997z+U5r7NS3ayEnUwdc3HfL/YUI+myTqY6islYkH8ykWF1HbEFccrLYf0VAOABUt0ImHwYP6esQERDSSwX1LPy10YvpKdTwt0r7ZpvXnJTkHVDqUleYLta0gPuLXh9Urrlcp729U16r5bXRJ9v5L6/U2J09c9AFSTMrjMELm+SnlfuD5p/ZXGSpkAPtjENyiElT7VkWEpAJAUxxM6DLsTMwwj0JgTMwwj0JgTMwwj0JgTMwwj0LTpxESkl4i8IyIrRWSFiNwQ1bNE5E0RWRf9X48NNgzDOMS02RRRRLIBZDvnFolIGoCFAGYAuBJAiXPuNyJyM4BM59yP97WtmB7JLvT1o1tod1xyAbV9er4uz1n22lK+4WydyRg2Uk+4AYDNW3cq7bRJedR2I5nUk0DKlvJye9L18zdsVtrzF91FbStJRumxZU9T22VFRVRvzXNn3U31vPsuVlrvPrzsaN6sD5SWmNed2nbvkqG0giLdNA8AVt3+jF6fpM/jijB/zwenD1FagmdS0KZyneXNIqU1vm34MnPtbRgZH6unD0W2q/X8cp3dBICUeH2Nr96js6MAML7bcUpj0458k6RYc8vGJj19CADK60uV5pvYlBins7yldfz6YOVXB9wU0TlX4JxbFP26HMAqADkApgOYFTWbhYhjMwzD6FD26zkxEekLYBSATwB0d84VRH9UCID+eRaRmQBmAgDS4g90Pw3DMCjtDuyLSCqAFwB8zznX4olPF/lMSj+XOucedc6Ndc6NleSOfbbWMIwjn3Y5MRGJR8SBPeWc2zvZY2c0XrY3bta+QI1hGMZBpM1bIxERAI8DWOWc+12zH70M4AoAv4n+P7utbcXGxKBTasuA3b+WL+E7Fqd7HfWayIP1rL9WfDw/tKQEHaTdEebTbBYv0QHhc8/UQdMmT3KEJQF8k5XmFZJEhieAX1GuA8orV+crzU3jJU6sH5ivlIgF8WuW6OQIAGweREpI8nnPq9gY/f42Qe/vUZ2OoetZ369UMgEJAOJjdBhDPO+DkP5YbL8AwJEPH+xKYOVUAMCSan3TBrb7tfK68HKoUGyi0lgAnU1mAoBw7e52vT4AZCboXnysvAjgU5TS4jPabeujPZ/vJgG4DMAyEVkS1W5FxHk9JyJXAdgM4MJ2v6phGMZBok0n5pz7APAMcQSmHNzdMQzD2D/siX3DMAKNOTHDMAKNOTHDMAJNhz641VDbgKK1LTNb3z97KrW9ZZYuS+mXy8tdkpN0+cbyVfnUtn6HzpYV7QpTW+zUWcBJuYOU1i05iy5//OV3lJZxIS93Oa7HRKVd9+ij1LahRpeL9OmrGziy8hGATyViDQ0BoFe2nnJDs5AAsF6XoCTn8XKmtPhOSqtr1M0efaUxpXVhpaV4spPzi+YrbXIOD+eyKTu+DFoZ2Yed1bq5ZHYyL0tj5Tm+7LWQsLRvWlGj0yVCbLu+DOD+lCixTKTvGBpI6VK109ciAMR7Sr0YdidmGEagMSdmGEagMSdmGEagMSdmGEag6dDAfigphJy8Pi201zfynkiTJgxT2qaNehINANTX64DhhGOHUtvN+TrwOnhQLrVdsFgHu59dpMuDvjKYl0OdMGmE0nz9kxYULVDa9CnHUtvd1dVKe/9D3XeLjY0HgF79dRLAN5Vo7kefadFTSsSC+FWL+RSlWjLlhgX7fcHrHsk5VGdMyTlFaay8CAAyyFSgGM8+pJP97ZygEyGNnqlTcaQcimkAIpPCWrE//cBiyP2KL3jO+6TxsiMWxPfZsn5ivslIvilIdB/abWkYhvElxJyYYRiBxpyYYRiBxpyYYRiBxpyYYRiBps1pRweTvDF5bs5Hb7bQZr75E2r78JQ7lObL8lQ06GxZqqfhGy318PhyNsmFZbVm/OMmuv61rz2ktN4/5WVW/UnZ0EdX/y+1ZeUbrPHfObO/Q9c/NfVOpcV6skQV9TrDyRoaAjy7yLKQANBzhm4uWThbZ359pVNsu76yIzbtqLunFIhN9WFlPABQ16TLpFh5kG+/QmQKku99qGnUGenkOD0BCeBZS3YtxwvPhCaR7TZ5miLWk1IxX4aVlTltr9xKbdk0qgGdhhzYtCPDMIwvM+bEDMMINObEDMMINObEDMMINB0a2B81Js+9O29OC40FLAFejsACoQAvn/D1SmJBfFZm4SOJjFdn02UAX1C8/WUWvqA4S06wYKyvr1M9mb7jm8iTQgLrvuk/rB9YgufcVJJkTI9zJijtB3dcStdP7qX7r724TvdvA4A1xbuU9sOJ51Jb1h9rYCfdQw4Aksj7MOG+q5T25NU30/Wn/+yHSvv2FdOo7bUjLlOaLznxxtbXlPbqBt1T7eoR/Bw8u/oVpU3I4VOn1uzOV9opfY6ntm9v+VBp973wKrVdffuzSuubNtgC+4ZhHHmYEzMMI9CYEzMMI9CYEzMMI9B0aD+xuqY6bK3Mb6H1TeW9uHbV7FTasp2ktxWA47qfoLR6xwPVWyvylebradQvXQd0V4eXK21ohu4bBgALi3UwdWxX3iOM7UNJAx+iwALz7Clp35PmSbE6CVBSX0xt8ys2KO2oTjzIy54U9/UDY0/isyD+vXc8RdePemiI0uoaeUXH+UPHKO251TwJcM3I85V20dM/pbYDeun+addM1QNI7vnkebr+lGnjlfanJ/5NbWcPXqy0Hj34gJqiIj3sZOKIwUqrJYkYgJ/Hggp+fcya84HSZmcsobZxcfpaGD9Ov48AcM+Ch6nOsDsxwzACjTkxwzACjTkxwzACjTkxwzACjTkxwzACTYdmJ2MlFmnxLXsrvbPjTWqbQEqMTu55KrVlGbQNpeup7bhuOiO0gIy5B4D1ZBvT+sxQ2vbKzXR9zxRdFpIWyqC2bBsLixdS29bnEABOzD5Jab4+Vm9vf53qjK9kT1ZaSS3PVJXWhZXmm0oU5/Slx0qJWBYSAC69/jalffS/T1Lbp1f/n9J+cdx3qe03/6N7raWl875duyt09rgqS5eKVVTx8rEbjztbaVeN1NlNAOgU0v3xSuv4NKusxEylffd53dtuXhd+fV1+zDlK65LYhdpOnqmfDFhZwieY/eR5nWm+8WR9DgDgtheepjrD7sQMwwg05sQMwwg05sQMwwg05sQMwwg0HdxPbJR7r1U/sdK6EmqbSoZO+HpTNZEBIs432ICU7Pj2gQ1iiI/RCQfW3wsAyurDSguR9QCwpWIjeS0+Zp4NUdhKEgO9U/rS9Wygh6+nGksOVJChEwA8Z5zD9uG7792qNF8p0TUjL1DacV//OrVd/4JOHo3+zZXUtuAXbyvtxx/eTm2nD+RB+Nas3cOTTL/7jy4xig/xXNuQ/rlKO3NAHrX9zuN/0a915ZVK++aQ/6Hrky4apsV0fi3mDtQDbhJCfFDI+RNHK210D17CtiG8RWk/GXub9RMzDOPIw5yYYRiBxpyYYRiBxpyYYRiBxpyYYRiBpkOzk/2G9XG3v/CTFlpel5HU9p9rdanIeYOmUlvW3O2tLXOpbU5qN6Wt28PLhnLStO3Fgy5WGmsGCAD3LvyT0m4ddxO1ZWPmH13+d2pb36gzrD8e8z2lJZHGgwDw4qbnlPZpAS8VYVN24j1j6ueT8q0pOadQ25La3Uq75vV7lMYaGgLA+j3blfa9UTzbNvB8Xa5W+PIn1Pakx65W2u/P/Ra1nbvtU6X9+l5dWvPb2/j6xYX6urt+lM66AsDRGTqL58vWs7KwFzfMVtqiwk10/c3H6vPYPUlnIQGgqLpQaV0T9e8NANy/RJc+vb5yJbWddZ5uRDksa7RlJw3DOPIwJ2YYRqAxJ2YYRqBp04mJSKKIzBeRpSKyQkR+HtX7icgnIrJeRP4hIvyRXsMwjENIm4F9EREAKc65ChGJB/ABgBsA3AjgRefcsyLyCIClzrl9jigZOXqEe/3DlmPLfaU1QrQqz/SfrISuSvMF21kA3VcwkxCjA6e7SNC0W6KeegMAVQ0VSiv1lOwkkdIlXwA9gdiuDq9Qmu+9HdVlnNIaSDkWwM+jCP/bx8q/Yjy2ceR9/6jwfaX5phKxfmB5d11Obdf+9EWl9ThH95UDgD2vLFXanfN/S21PyFUxZozsnKe0B5Y8RtenhfT7+OpyPU0LAI7O0dfYtjLeT2xnoS6jm3XJLUobkjGcrj9p1jeVtuhtPmls8LEDlea77m6dpidJ5aTwfnO7anYp7WuDrjywwL6LsPe3MT76zwGYDGDvLKpZAGa0tS3DMIyDTbtiYiISKyJLABQBeBPABgBh5z4fbLgNAHephmEYh5B2OTHnXKNzLg9ALoBjARzd3hcQkZkiskBEFuzexbtFGIZhHCj7lZ10zoUBvANgIoAMkc/HVucC0E8fRtY86pwb65wb27kLn1hsGIZxoLQnO9lVRDKiXycBOBXAKkSc2d7Hi68AoB8JNgzDOMS0Z9pRNoBZIhKLiNN7zjn3bxFZCeBZEbkTwGIAj7e1IQensmAhT3Yyv1xPMOqd2o/aFlXvUNqeWv7RNStRNxTcSUonACAzpO8cS2p0uUxOcm+6flvlVqUNy8qjtkL+nqwM60xZxFbnbgemD1ba/pSlFFTpcwjwDFYkYa0pq9ujtIyQPt8AUNNYrbQm16S0a0bqjBbApxKxhoYAMOahC5XGspAAkHmWLoO7667rqO3P33xGaWnpLystK5k3zfzl8Zco7boRM6ltrMQqbVsFL5frT66FFzb+U2lnP6gzlgCw6bb/KK3ucl3a59uvyvpyavvQMp2l3VLGf/dy07pTndGmE3POfQZgFNE3IhIfMwzDOGzYE/uGYQQac2KGYQQac2KGYQSajp929HHLMpKCKh38BoDOpJQoTngILzZG641NDcQSaIQOHpfU6EA3AGSSqULbK/UUltyUPnR9Sa0unUiL1+PoAaCgSj+h4gvMdwplUL01vtKrLqRMyjfBiE0laiLnEAAayDn3lZWVk0lQrMfYRU/rvlIAkJauJ1GN7a0nAgHAtP4nKu2tzR9S2y5JGUq75WbdBwsAcJS2feNmXaJ0+V952dIfv36N0u7/WPfRA4BtW4uU1rmzfm8AoLJSJ006ddK95X4+hU+Hmr3+Xf1aSbw33cpdO5W2cLlOygFAQREph7rue9T2a3+4V4t/XG79xAzDOPIwJ2YYRqAxJ2YYRqAxJ2YYRqAxJ2YYRqBpT9nRQaPRNaC0VQYqxTORZ2tFvtI6J3ahtjGk9KG8njeMY6Uta8Nrqe2YrnrSzooSPZ2lX9ogun7JrsVKO9kz/Ycd28LihdQ2MyFDaQmxCUpjpUgAUNtUo7Q1pXza0TGZI5TmPE0ky+rCSksn2U0AqGvSZSysMeSAXrzh5O4K3SBz+sAp1PadLR8pjTU0BHgpEctCAgDWhpW0Lqwzc/Hx/NdsQHp/pZ1/DJ/utL23zk7WNuomlACQnarL5Yqrwkp7csXrdP3lw85UGvu9AYBJPfW1kNedN1B8Zt7HSqtr5Bn0kcfoc7MUvGGk3YkZhhFozIkZhhFozIkZhhFozIkZhhFoOjSwX9dYhy2teiBlJvBur6vDa5T2lZ58lDrrr7VqDw9Ud07Ur7enNkxtl+5eorRpfc5RWr3jk4IqG6qUFuspnaojJULF1bpsCQBKanTfrgsHXtzu11q06xOlrQ2vp7ZDM4YpzVeoxvqysfIxAJBGfW4m3HeV0q6ZyoP1VVk6OeHj1/c+pbRNT/DJSKwfGCslAngQ//rv3620B3//I7r+4v+9Q2k/P/tr3HaQ7omWEpfG96tMX/tPrnpJaRmJvKyttlEnXUZ01n3WAGBDqb5urhzCj6GoMqy02//1LLV9ZaYuOxp2jX5vALsTMwwj4JgTMwwj0JgTMwwj0JgTMwwj0HRoP7G8MXnu7Y/eaKE1eHpesb2Kl3hqy57Yb/AE21mfMdbLCwCS4pKVtov0HhvdZTxdv6l8ndIqGyqIJdAzWc8e3uHZryzS56yyXm/XV4kwOfc0pfn6iXUiw1JaD3vZSznZRnp8BrVlLNw1X2n3fPI8sQQqqnRg/7JRk7htvU4iFFTo3mUAkB8OK+3jxTrJBPAn8W8+e4bSrr9RB/sB4PlH7lHaG/k66QIAg7P09bFqF+/FNzBTVzn8a8USpX1/0tl0fQxJlC3fpa9lAKhp0EmATwv40JlPF+iEw9QTeYVCZb32C6/M+Jv1EzMM48jDnJhhGIHGnJhhGIHGnJhhGIHGnJhhGIGmQ8uOAIcm17IHUjUpzQGAODLBKCXEe4+xiTphMjkHACA68+Lrj8V6dI3tOpFvl8BGvA/JGE5tWf80X6+lmkadmctJ6a20gelH0/XVpOTH91qhGH0OfBntRNIPLC7Gl1HWfz9P/9kPlXbKNJ75vfE4nVm75aW/U9txxwxQ2qAsXg71y+MvUdrqY1ZTW9YPjJUSsSwkAFzwLX284742gdoel3OM0m4bP53aXvf2b5Q2NLu70o7J0tsEgLMe1ft16lhdfgYAG/boErhT+h1FbftnZCht/rp8avuH6dcr7RX8jdranZhhGIHGnJhhGIHGnJhhGIHGnJhhGIGmQwP7zunAftdEPgiinpQjrfMMs+hPBmKkxPNeS6w0JielF7VNIyUz4To9il2nCiIkxelANyt7AoCeyblK8w1GiSOJjAQSgGcBfABIjNXlVF0S+aWQX677RfVNG0htWbDeF9hnPeC+fcU0pf3piX/T9VeN1H3G4kP8GK4fdYHSvvfKg9T2uhEzlXb/xzwwz4Z6sH5gvlIiFsT/9Bk9TAMAfrRZ92rLzX2N2i5ftUlpl55xvNIySUkZAHTrlqm07eXl1Hbuh3ooyIrlG6nt4KN08mlEP33dA8DDS3T/Mx92J2YYRqAxJ2YYRqAxJ2YYRqAxJ2YYRqAxJ2YYRqDp4LIjoKFVds6XQROS6QqRMiCAN0VsdHzEe+vsKOBvtshgDQFZps2HbzJSaZ0u38jwZI84eh8qG3hGiZ0v3wyjlPgUYslt6XnwlCjVNOnSqWtHXKa02YMX0/WdQulKG9KfZ7qOztDlNUfn8Kw4KxXbtrWI2m7vrXU2lWh7xU66npUSsSwkABR9mK9F3gMSdeFqpXVP1ecrRLLcAJCaprPXI7vzSWPLuupMZmGxvpYBoHuaLhu8cax+zwHgiud+TXWG3YkZhhFozIkZhhFozIkZhhFozIkZhhFoOjSw3+gaUdEq2JxBJvcAQHVDpdLYyHQA6JHUU2m+YHsl2W5yIu9TxoL4cwveV9r4brznVUKsHhPv26+c5D5Kq2vSk2QAwLkmpZXXh5VW75lKVEUC/ilxOvALAKv36FKvvC468AvwoDgrHwOAzARdUtU9Wb+PPXrw5EZpXZnSzhyQR23Z+7CtTK8HgG0Vm5XWuXMnalvbqJNEKXG63M03lYj1A/OVErEgftFH+dT0hG9+RWk5qd2UVl7Pz8ENY89S2tJi3lNt+NB+SkvdosvtAKAv6Sfm+33oSkqffNidmGEYgcacmGEYgcacmGEYgabdTkxEYkVksYj8O/p9PxH5RETWi8g/RIQ/OWcYhnEI2Z87sRsANI/y/hbA751zAwHsAXDVwdwxwzCM9tCu7KSI5AKYBuBXAG4UEQEwGcDeDnCzANwB4OF9vlhMHLoktMySlNeFqW2IZJQmdOd1FqnxOrNW58uKhXQ21DdxiWVOT8s9Q2msRAoAKkgDRh+rw8uU1jVJZ5QAIBSjzw2bSlQfw89BVoLeLssGA8D4bsfp1yLvDQA0Ot3wMTmOZ35Z1vKNrTozV1TES1iyEnX26spH7qe2p91xqtJ2FurmlgBvsFlZqct4ACA7VWdO15XpbO7ATF7ixKYSsYaGAC8lYllIAJj7+HtKO/2uIUrLIhliADjv/juVNmSQbmgIAOs2bldaXRXPqrOysOKaYmpbV8cz64z23ondD+BHAPbm9jsDCDv3+VW7DUBOu1/VMAzjINGmExORswAUOecWHsgLiMhMEVkgIgt2F/O/foZhGAdKez5OTgJwjohMBZAIIB3AAwAyRCQuejeWC0DfVwJwzj0K4FEAyBszkrc0MAzDOEDavBNzzt3inMt1zvUFcDGAOc65SwG8A2DvBIYrAMw+ZHtpGIbhQXwj6amxyEkAfuCcO0tE+gN4FkAWgMUAvu6c4xG9KKPHjHLvffxOC+39gneo7cTuejoLC14DvDyHleYAQGKcLpmpqud9tzZX6CDroE46QFrj6YnGgtqsbxgArCtdo7TBnY6mtqwPVDzptRau3U3XN5AAPEt4ALyHW4WnXIVNO4rx/J1kfdV+PPdupTU08fdx8Zp8pX33lDOpbW2jTiJM6slLxZbvXqm0P3/8FrU9bkBfqrdm3iZdygQAQ7O7t2s9wPuBsVIiACit1dfzbbc+orSlz/KJQm9ueVdpo7sNp7aPffay0sI1ulccALz2wgdKm3SmnhgFANUkObDo6pcWOufGttb3q3bSOfcugHejX28EcOz+rDcMwzjY2BP7hmEEGnNihmEEGnNihmEEmv0K7H9RRo4e4V6d2zIQyJ62B4CNZWuVlp3CB0EwdlXz4QxJJLBfWFVAbYdk6mBmfrnuaTa401C6ft5OHcj0VR3UNOonsteTYD8AxMfowSbsuFifNYD3/VpTqgPaADCIJBfiPAMmqhoqlJZIqgsA/sT+qrDeh9pGniuaV6AfW/zR6B9Q26vf+qHSHpr8K2o78Fe6l9asq/R6AHhyxetKy0jUx3tCr1F0/TFZelBIpmc4DEvm+PqBsSfxt1TqJNXIi8+l68tf0e9DE3iCJYb2A+M9wuYXfagthdsmkITSpB6TaWDf7sQMwwg05sQMwwg05sQMwwg05sQMwwg05sQMwwg0HTrtKEZiVDZyxZ6l1HYwKe/x9aZiU4lyUvT0IABwJMvS5ClRqmvU5RN9UvuTbfIM77Asnd1kWUgAmLNdl7YMzdTnAAAyQrqX1i7Sl4llCwHeJ61Pmj4ugJ9zXzlTbIy+nKo9JVlp8XqC0LOrX1FaHZkoBACXH3OO0pIuGkZtlz/2gtJOmvVNarvptv8o7aa5P+X7MEyXObFsak0DL8M561Gd9ezmmfKTmqazz2wqEcD7gd195WVKY1lIAEibqrPt3Sb1pbbpZL9i43T2GwDOHK3fn9P76vJCAHh7i85k+rA7McMwAo05McMwAo05McMwAo05McMwAk2HBvYB3edrQPogahdPyizYIAqAl9HEEA3gvb9Cse2fNlfdqAdqsB5lkX3QfyNqSbIAAIaTJEAcCZQDfFDHmrAu00IGXY6shK56m55SosYmfc59iQxWSuQra2si25iQo8twCir4IIkuiWTIRTo/hu5J2Upb9PZn1Lbuch2Y75zEE0osITSi80il/XXFk3T9qWN1oHt7Oe9tN7K7PoalxaupLRvqwfqB+UqJWBC/6MN8aptz4Til+YZ8DOsyQGmdQjrBAwCDMvlgEobdiRmGEWjMiRmGEWjMiRmGEWjMiRmGEWjMiRmGEWg6NDvp4NDoWpaRxIlu8OejqJo3L+yaqMfE+5qtCckYZob4OPc40nyQZTfF0wSOZUhLavkxsExXr9S+1JZxVp/pSvuYNKEDAJBz42t0WF5fqrRM0nQP4MfAMrQAUEkmTK3Zna+0WXN0Y0kAmDzzBKXlDtQZPAAoqi5U2uBjB1JbluleuYs32JzUU2dYN5Tqppk1Dbyx44Y9evLV3A951nRZV12ONHxoP2q7bqMeAcumEo2bMpGuZ6VELAsJAIv/+akWe6ZQ22d6zFPamB751NaXmWfYnZhhGIHGnJhhGIHGnJhhGIHGnJhhGIGmY/uJIQahVlNMKj09r5JidHAwPZRBbfen7Gh/3HbrEikAaCBlOI0xvBwqFKMntsQKP+X1TbrPmG8OVRzZButT5psUBFIKxLYJAImxSUrz9V9jQXxf2RFLmpzSR/eWmp2xhK5fWbJKaQkhniTqmtit3fvFEg4Ll2+gtnnddRD+yiFfU9qdBc/Q9af0O0ppK5ZvpLaFxToJkLpFvzcAUFel3/dwDSt34wkp1g/MV0pEg/g7dGkeAOzaFVba+NG6JxsA/HrOc/z1CHYnZhhGoDEnZhhGoDEnZhhGoDEnZhhGoDEnZhhGoOnwpoitS3TWl66hdkdn6AZ5KXFp1LaBNEusbuBTdlizxd01RdS2hjQw7JakS5xYM0CAN0BMbzXt6b/7oCcIba3YRG1ZIzk2lWhwp8F0fUVDmdJ8GUdWYuSbosQyt76GkWwbbMJNnGdyzk+ef0ppl540gdrev+Qhpd067Xxq+9Cyx5RWUFRCbZ+Z97HSiirDSvt0gc6kAkD/jAylDT6KNwPsnqbf375kPQAM6Z+rtJf+8a7S5o/jZWlsKhFraAjwUiKWhQSApc8vVNofMvnvdGEhP+cMuxMzDCPQmBMzDCPQmBMzDCPQmBMzDCPQdGhgvwkOtU0tSyKGZuopLAAvCymp5ZNv0uJ1oNtXRlPXpEsykuP5NJsUorPeVD2Sc+j61r3TAGBzRT61PSrjaKX5gu2sdCkxVgfQfSVd3cj0nzrPFKbSOh1gTYvPoLbVTpeb+N6H7ZVblXbfC68qbfy4IXT9jSefrbSEWF3mBQD3vPuS0k46nffS2lKm399Z132P2tY16oTO7f96VmlTTxxD189fl6+0Ef10UB4Abhx7mdJ8feyKa/TvSVGZLqfy9dw7va8u//JNJWL9wHylRCyI/95f3qW2S5/V79nI246ltnYnZhhGoDEnZhhGoDEnZhhGoDEnZhhGoDEnZhhGoBFfc7hDwegxo90Hn7zfQntzm85IAcCJ2ZOV1rqh4l7YRB5fVoyVLrEyHMBTNhTSU2c+3jmXrh/fbZLSfBmlVeFlSuuTyqfZsIaCCbGJSvNlN8vqdIO9pDg+oYaVafnKrJgtaywJACW1u5QWG6NLjO5Z8DBd//K8JUq75rQp1HZ6/6lK+2w3nypU3aDf86se+hO1HXlMf6U9deEdSvvx+3+k638w7iKlPbxEZ+UAYGW+nmDUtZu+FgHewLCaNEr849nfoetf3TRHaYMyeTnU2pLNSnt95Upqy0qJXr32Hmo78uJztfjm9oXOubGtZbsTMwwj0JgTMwwj0JgTMwwj0JgTMwwj0HRoYF9EigHsjQR2AaCju8HHjit4HKnHdqQdVx/nXNfWYoc6sRYvLLKAZRqCjh1X8DhSj+1IPa7W2MdJwzACjTkxwzACzeF0Yo8extc+lNhxBY8j9diO1ONqwWGLiRmGYRwM7OOkYRiBpsOdmIicISJrRGS9iNzc0a9/MBGRv4pIkYgsb6ZlicibIrIu+j8vcPsSIyK9ROQdEVkpIitE5IaoHuhjE5FEEZkvIkujx/XzqN5PRD6JXpP/EBFdBBoARCRWRBaLyL+j3x8Rx9UWHerERCQWwIMAzgQwFMAlIjK0I/fhIPMEgDNaaTcDeNs5NwjA29Hvg0YDgJucc0MBTABwffR9Cvqx1QKY7JwbCSAPwBkiMgHAbwH83jk3EMAeAFcdvl38QtwAoPmQyyPluPZJR9+JHQtgvXNuo3OuDsCzAKZ38D4cNJxz7wNoXZo/HcCs6NezAMzoyH06GDjnCpxzi6JflyPyi5GDgB+bi7B38EB89J8DMBnA81E9cMcFACKSC2AagL9EvxccAcfVHjraieUAaD4hYltUO5Lo7pwriH5dCKD74dyZL4qI9AUwCsAnOAKOLfqRawmAIgBvAtgAIOzc52Pkg3pN3g/gRwD29j7qjCPjuNrEAvuHEBdJ/QY2/SsiqQBeAPA951yLpmtBPTbnXKNzLg9ALiKfDPSYqYAhImcBKHLOLTzc+3I46NCRbQC2A+jV7PvcqHYksVNEsp1zBSKSjchf/MAhIvGIOLCnnHMvRuUj4tgAwDkXFpF3AEwEkCEicdG7liBek5MAnCMiUwEkAkgH8ACCf1ztoqPvxD4FMCiaNQkBuBjAyx28D4ealwFcEf36CgCzD+O+HBDReMrjAFY5537X7EeBPjYR6SoiGdGvkwCciki87x0AF0TNAndczrlbnHO5zrm+iPxOzXHOXYqAH1e7cc516D8AUwGsRSQW8ZOOfv2DfCzPACgAUI9IzOEqRGIRbwNYB+AtAFmHez8P4LiOR+Sj4mcAlkT/TQ36sQEYAWBx9LiWA/hZVO8PYD6A9QD+CSDhcO/rFzjGkwD8+0g7rn39syf2DcMINBbYNwwj0JgTMwwj0JgTMwwj0JgTMwwj0JgTMwwj0JgTMwwj0JgTMwwj0JgTMwwj0Pw/8i01t3libwoAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ "
" ] @@ -425,13 +420,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "

Version Information

Qiskit SoftwareVersion
qiskit-terra0.19.0
qiskit-aer0.8.2
qiskit-ignis0.6.0
qiskit-aqua0.9.2
qiskit0.27.0
qiskit-machine-learning0.3.0
System information
Python3.7.9 (default, Aug 31 2020, 17:10:11) [MSC v.1916 64 bit (AMD64)]
OSWindows
CPUs4
Memory (Gb)31.837730407714844
Fri Dec 03 15:08:20 2021 GMT Standard Time
" + "

Version Information

Qiskit SoftwareVersion
qiskit-terra0.22.0
qiskit-aer0.11.0
qiskit-ignis0.7.0
qiskit0.33.0
qiskit-machine-learning0.5.0
System information
Python version3.7.9
Python compilerMSC v.1916 64 bit (AMD64)
Python builddefault, Aug 31 2020 17:10:11
OSWindows
CPUs4
Memory (Gb)31.837730407714844
Mon Oct 10 12:01:53 2022 GMT Daylight Time
" ], "text/plain": [ "" @@ -443,7 +438,7 @@ { "data": { "text/html": [ - "

This code is a part of Qiskit

© Copyright IBM 2017, 2021.

This code is licensed under the Apache License, Version 2.0. You may
obtain a copy of this license in the LICENSE.txt file in the root directory
of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.

Any modifications or derivative works of this code must retain this
copyright notice, and modified files need to carry a notice indicating
that they have been altered from the originals.

" + "

This code is a part of Qiskit

© Copyright IBM 2017, 2022.

This code is licensed under the Apache License, Version 2.0. You may
obtain a copy of this license in the LICENSE.txt file in the root directory
of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.

Any modifications or derivative works of this code must retain this
copyright notice, and modified files need to carry a notice indicating
that they have been altered from the originals.

" ], "text/plain": [ "" @@ -462,7 +457,6 @@ } ], "metadata": { - "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3", "language": "python", diff --git a/docs/tutorials/07_pegasos_qsvc.ipynb b/docs/tutorials/07_pegasos_qsvc.ipynb index d004da504..2f8bf916c 100644 --- a/docs/tutorials/07_pegasos_qsvc.ipynb +++ b/docs/tutorials/07_pegasos_qsvc.ipynb @@ -7,7 +7,7 @@ "source": [ "# Pegasos Quantum Support Vector Classifier\n", "\n", - "There's another SVM based algorithm that benefits from the quantum kernel method. Here, we introduce an implementation of a another classification algorithm, which is an alternative version to the QSVC shown above. This classification algorithm implements the Pegasos algorithm from the paper \"Pegasos: Primal Estimated sub-GrAdient SOlver for SVM\" by Shalev-Shwartz et al., see: https://home.ttic.edu/~nati/Publications/PegasosMPB.pdf.\n", + "There's another SVM based algorithm that benefits from the quantum kernel method. Here, we introduce an implementation of a another classification algorithm, which is an alternative version to the `QSVC` available in Qiskit Machine Learning and shown in the [\"Quantum Kernel Machine Learning\"](./03_quantum_kernel.ipynb) tutorial. This classification algorithm implements the Pegasos algorithm from the paper \"Pegasos: Primal Estimated sub-GrAdient SOlver for SVM\" by Shalev-Shwartz et al., see: https://home.ttic.edu/~nati/Publications/PegasosMPB.pdf.\n", "\n", "This algorithm is an alternative to the dual optimization from the `scikit-learn` package, benefits from the kernel trick, and yields a training complexity that is independent of the size of the training set. Thus, the `PegasosQSVC` is expected to train faster than QSVC for sufficiently large training sets.\n", "\n", @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "impressed-laser", "metadata": {}, "outputs": [], @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "adolescent-composer", "metadata": {}, "outputs": [], @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "dying-dispatch", "metadata": {}, "outputs": [], @@ -98,34 +98,28 @@ "source": [ "The algorithm will run using:\n", "\n", - "- A statevector simulator\n", + "- The default fidelity instantiated in `FidelityQuantumKernel`\n", "- A quantum kernel created from `ZFeatureMap`" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "id": "automated-allergy", "metadata": {}, "outputs": [], "source": [ "from qiskit import BasicAer\n", "from qiskit.circuit.library import ZFeatureMap\n", - "from qiskit.utils import QuantumInstance, algorithm_globals\n", + "from qiskit.utils import algorithm_globals\n", "\n", - "from qiskit_machine_learning.kernels import QuantumKernel\n", + "from qiskit_machine_learning.kernels import FidelityQuantumKernel\n", "\n", "algorithm_globals.random_seed = 12345\n", "\n", - "pegasos_backend = QuantumInstance(\n", - " BasicAer.get_backend(\"statevector_simulator\"),\n", - " seed_simulator=algorithm_globals.random_seed,\n", - " seed_transpiler=algorithm_globals.random_seed,\n", - ")\n", - "\n", "feature_map = ZFeatureMap(feature_dimension=num_qubits, reps=1)\n", "\n", - "qkernel = QuantumKernel(feature_map=feature_map, quantum_instance=pegasos_backend)" + "qkernel = FidelityQuantumKernel(feature_map=feature_map)" ] }, { @@ -142,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "id": "representative-thumb", "metadata": {}, "outputs": [ @@ -177,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "id": "judicial-pottery", "metadata": {}, "outputs": [], @@ -200,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "id": "competitive-outdoors", "metadata": {}, "outputs": [], @@ -219,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 8, "id": "monetary-knife", "metadata": { "tags": [ @@ -229,7 +223,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -288,14 +282,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 9, "id": "imperial-promise", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "

Version Information

Qiskit SoftwareVersion
qiskit-terra0.19.0
qiskit-aer0.8.2
qiskit-ignis0.6.0
qiskit-aqua0.9.2
qiskit0.27.0
qiskit-machine-learning0.3.0
System information
Python3.7.9 (default, Aug 31 2020, 17:10:11) [MSC v.1916 64 bit (AMD64)]
OSWindows
CPUs4
Memory (Gb)31.837730407714844
Fri Dec 03 15:05:23 2021 GMT Standard Time
" + "

Version Information

Qiskit SoftwareVersion
qiskit-terra0.22.0
qiskit-aer0.11.0
qiskit-ignis0.7.0
qiskit0.33.0
qiskit-machine-learning0.5.0
System information
Python version3.7.9
Python compilerMSC v.1916 64 bit (AMD64)
Python builddefault, Aug 31 2020 17:10:11
OSWindows
CPUs4
Memory (Gb)31.837730407714844
Thu Oct 13 10:42:49 2022 GMT Daylight Time
" ], "text/plain": [ "" @@ -307,7 +301,7 @@ { "data": { "text/html": [ - "

This code is a part of Qiskit

© Copyright IBM 2017, 2021.

This code is licensed under the Apache License, Version 2.0. You may
obtain a copy of this license in the LICENSE.txt file in the root directory
of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.

Any modifications or derivative works of this code must retain this
copyright notice, and modified files need to carry a notice indicating
that they have been altered from the originals.

" + "

This code is a part of Qiskit

© Copyright IBM 2017, 2022.

This code is licensed under the Apache License, Version 2.0. You may
obtain a copy of this license in the LICENSE.txt file in the root directory
of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.

Any modifications or derivative works of this code must retain this
copyright notice, and modified files need to carry a notice indicating
that they have been altered from the originals.

" ], "text/plain": [ "" diff --git a/docs/tutorials/08_quantum_kernel_trainer.ipynb b/docs/tutorials/08_quantum_kernel_trainer.ipynb index 5dc5abb1d..270a26c85 100644 --- a/docs/tutorials/08_quantum_kernel_trainer.ipynb +++ b/docs/tutorials/08_quantum_kernel_trainer.ipynb @@ -14,7 +14,7 @@ "\n", "1. Prepare the dataset\n", "2. Define the quantum feature map\n", - "3. Set up the `QuantumKernel` and `QuantumKernelTrainer` objects\n", + "3. Set up an instance of `TrainableKernel` and `QuantumKernelTrainer` objects\n", "4. Use the `QuantumKernelTrainer.fit` method to train the kernel parameters on the dataset\n", "5. Pass the trained quantum kernel to a machine learning model" ] @@ -36,21 +36,17 @@ "source": [ "# External imports\n", "from pylab import cm\n", - "import pandas as pd\n", "from sklearn import metrics\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "%matplotlib inline\n", - "\n", "# Qiskit imports\n", "from qiskit import QuantumCircuit\n", "from qiskit.circuit import ParameterVector\n", - "from qiskit_aer import AerSimulator\n", "from qiskit.visualization import circuit_drawer\n", "from qiskit.algorithms.optimizers import SPSA\n", "from qiskit.circuit.library import ZZFeatureMap\n", - "from qiskit_machine_learning.kernels import QuantumKernel\n", + "from qiskit_machine_learning.kernels import TrainableFidelityQuantumKernel\n", "from qiskit_machine_learning.kernels.algorithms import QuantumKernelTrainer\n", "from qiskit_machine_learning.algorithms import QSVC\n", "from qiskit_machine_learning.datasets import ad_hoc_data\n", @@ -102,7 +98,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -228,7 +224,7 @@ "source": [ "### Set Up the Quantum Kernel and Quantum Kernel Trainer\n", "\n", - "To train the quantum kernel, we need a `QuantumKernel` (holds the feature map and its parameters) and `QuantumKernelTrainer` (manages the training process).\n", + "To train the quantum kernel, we will use an instance of `TrainableFidelityQuantumKernel` (holds the feature map and its parameters) and `QuantumKernelTrainer` (manages the training process).\n", "\n", "We will train using the Quantum Kernel Alignment technique by selecting the kernel loss function, `SVCLoss`, as input to the `QuantumKernelTrainer`. Since this is a Qiskit-supported loss, we can use the string, `\"svc_loss\"`; however, note that default settings are used when passing the loss as a string. For custom settings, instantiate explicitly with the desired options, and pass the `KernelLoss` object to the `QuantumKernelTrainer`.\n", "\n", @@ -242,11 +238,8 @@ "metadata": {}, "outputs": [], "source": [ - "# Use the qasm simulator backend\n", - "backend = AerSimulator(method=\"statevector\")\n", - "\n", "# Instantiate quantum kernel\n", - "quant_kernel = QuantumKernel(fm, training_parameters=training_params, quantum_instance=backend)\n", + "quant_kernel = TrainableFidelityQuantumKernel(feature_map=fm, training_parameters=training_params)\n", "\n", "# Set up the optimizer\n", "cb_qkt = QKTCallback()\n", @@ -273,7 +266,7 @@ " - `optimal_value`: The value of the loss function at the optimal point\n", " - `optimizer_evals`: The number of evaluations performed by the optimizer\n", " - `optimizer_time`: The amount of time taken to perform optimization\n", - " - `quantum_kernel`: A `QuantumKernel` object with optimal values bound to the feature map" + " - `quantum_kernel`: A `TrainableKernel` object with optimal values bound to the feature map" ] }, { @@ -286,12 +279,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "{ 'optimal_parameters': {ParameterVectorElement(θ[0]): 1.588604609076821},\n", - " 'optimal_point': array([1.58860461]),\n", - " 'optimal_value': 15.538298602582271,\n", + "{ 'optimal_circuit': None,\n", + " 'optimal_parameters': {ParameterVectorElement(θ[0]): 2.9050378226285405},\n", + " 'optimal_point': array([2.90503782]),\n", + " 'optimal_value': 9.978740202676176,\n", " 'optimizer_evals': 30,\n", + " 'optimizer_result': None,\n", " 'optimizer_time': None,\n", - " 'quantum_kernel': }\n" + " 'quantum_kernel': }\n" ] } ], @@ -322,7 +317,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "accuracy test: 0.9\n" + "accuracy test: 0.8\n" ] } ], @@ -348,7 +343,7 @@ "source": [ "### Visualize the Kernel Training Process\n", "\n", - "From the callback data, we can plot how the loss evolves during the training process. We see it converges rapidly and reaches 100% test accuracy on this dataset with our choice of inputs.\n", + "From the callback data, we can plot how the loss evolves during the training process. We see it converges rapidly and reaches high test accuracy on this dataset with our choice of inputs.\n", "\n", "We can also display the final kernel matrix, which is a measure of similarity between the training samples." ] @@ -361,7 +356,7 @@ "outputs": [ { "data": { - "image/png": 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nnntqxE9EREREpAQUReAH3E4UeF3g7r9NF5rZzcBPgeHAuTWoZwRR0Hezu/8so54LiAKo24Ej69j2IKKg7xWgj7t/HR+/OfACcLmZ/cXdp2SccwvwgLuvMzvazIbG9Y8B9g5c02/cffaGLrwQKisreeGFFwrdDRERERER2YCCT/WMR9wOJxo9y3608Urga+B0M9tsA/VsDpweH39V1u5RwBzgCDPbtY5tp5+kHJ4O+gDcfQnwK8CAn2RW5O7XZwd9seuBZcBe8WhlSUmlUlRXV/PZZ58VuisiIiIiIpJDMYz4HRZvn3b3NZk73P0rM/sXUXB2IPBsjnoOBFrG9XyVVc8aM3sKGBi3l57uWZu2d4i360wZzSr7bo5+rtMM0bRPgNWBY75nZq3j/f8BnnP3xTWsf6PKTPBy+OGHF7g3IiIiksvc+S0YPLrLeuWjRydnGuzbN/82QtkXQ9k7neRsnwAjByefM2hQ8vGtewdSQuaweGxyWshQNsxRgeyZoeuGcH9zZYVMEsp4CeFMmaFzzmg/MbF8UlXvYBuh/oauvbo6uTxX9tXQOaHykGeeye94gJ5DkzNoVoxPPj7Xax66xvE9R+ZX2fhA40AVoX3hn6mCj/gBe8TbWYH978Xbzhuhntqck362LmkNg/Ro4s5m1jJQZ6aTgC2AV939y8AxtwPXATcA44GPzSzwK6RhVVZWAsrsKSIiIiJS7Ioh8GsTbxcF9qfLt9wI9dTmnL/F22GZwV08HTTz70OZ56zHzHYBfks04ndxwiEvAqcAHYhGMncDLon3jTKzgTnqHmhmU8xsysachrn11lvToUMHBX4iIiIiIkWuGKZ6lppbiUbqDgbeMrMniMZUjyaaurmIKKBcE6rAzLYD/k6U6XNQUkZPd78nq+gD4CYzexf4KzDczO529/WmiLr7GKKEMXTv3j28Mmo9SKVSCvxERERERIpcMYz4pUfV2gT2p8u/3Aj15H1OnMTlW8BIotG6HxGNzL0YlzeNyxcmVRgHfc8RTTO90N1vD7SdyN3/H1ANbAN0zefcjSGVSvHee++xZMmSQndFREREREQCiiHwezfehp7h6xRvQ8/h1aWeWrXt7kvcfai7d3b3Td19G3c/A9gU2Bx4w91XZldmZu2IlnzoSjTSd1uuC8ohPX8zZ6bThpBKpXB3ZsyYUeiuiIiIiIhIQDEEfs/H28PNbJ3+mNkWQC9gKfDqBup5lWhphF7xeZn1NCHKzpnZXn22nZZOB/TH7B1mtiPwD6IF5c/Nd6Qvo542cR0OfFibOupTZmZPEREREREpTgV/xs/d3zezp4kCs0FECU/SriYa1bojc808M9szPvedjHqWmNkDREs2XAX8LKOewUBH4Cl3/yDjnLzbjttvnb2kgpn1AS4D3gfuyNrXgSjI7ACc5e735bonZrYD0MzdP84q3xy4D2gBPOPu/81VT0OoqKigbdu2CvxERESK3JdfJmeHDy3bMGFCuC5/Jnk5AOuavBxA+/bJ9YSWbAAYMjQ5LX3P8cnnjA0szZAr7X6X9Ve3AGBe0urLQI8eyeUTk29Hzjbefju5fMCA5PIcmf0ZMSK5PLQsRc+eya/TqL7hC6moSD4n9P4JLf+QaxmL0NIXofdiqO3Q8hYQfg1D54TeP7mW1xgyJFDXpOQdVVWJxUyqTl5iIle/cil44Bf7CfAycJuZfReYCRxAtM7eLGBY1vEz4232b4ShwKHAxWZWCUwCugDHA/OJgru6tg3wjpnNAN4BlgP7Ab2BT4HjswNFoumdHYGpQEczuyqhzvvcfXb87z2BiWb2StyH+UAF0IdoHcEPgHMS6mhwZqYELyIiIiIiRa4oAr945K07cA1wJHAU8AlRBs2r3f2LGtazwMwOAq4E+gL/BywA7gWuyB5Bq0Pb4+JjDwY2AeYAvwZ+7e5JSV06xtv9468kLwCz43+/D9wN9ACOI1oaYinRM4mjgNuyF6kvpFQqxa233srKlSvZZJNNCt0dERERERHJUhSBH4C7fwT8sIbHBpekjwOvC+Ovem87Pv5S4NI8jg/2N0d/fpzPOYWUSqX45ptvePvtt9l3330L3R0REREREclSDMldpMQpwYuIiIiISHFT4Cd11qlTJ1q1aqXAT0RERESkSBXNVE8pXU2bNmXfffdl+vTphe6KiIiIBKxcmZytMpSdcHzV/cG67p+XnG0wlL0zJJTFEcLZOydNTn6CZnFFOENoSCib4qhBMxPLZ5KcojOUPROgumdyIz0HJKfpDGXo7NMn3Ea+QhlCB0xIztwJ4dc2dO2h6whl4oTcGT+T5Mo8GxK69tD1hX4+QtlXIfc1Jgm930ZWBFLCkvs9F6IRP6kXqVSK6dOns2bNmkJ3RUREREREsijwk3pRWVnJ4sWL+fDDgq8pLyIiIiIiWRT4Sb1QghcRERERkeKlwE/qxV577UXTpk0V+ImIiIiIFCEFflIvWrRoQdeuXRX4iYiIiIgUIXPPPwOSlI7u3bv7lClTGqStAQMG8PTTT/PJJ580SHsiIlI6zGyqu3cvdD8aM7PuDut/JsiVWTNf1dXJ5ZMmBY6vCKRMBGaOTT6poiL5+NZtkrN95nL/2EDm0EB/Q+WDB4fbCGWRDGWkDGWXTMrIuiE9eiSXT5yYXJ4rq2Yoi2To/RO6V7mEsqyG6gpl3Ay9RyB8T7p2ze/4UF8BRo1KLg/d9+B7ujo52ydARe/kjJ/z5oV/12rET+pNKpXi008/5dNPPy10V0REREREJIMCP6k3SvAiIiIiIlKcFPhJvamsrATQQu4iIiI5mFk/M/utmb1kZovNzM3sDxs452Aze8LMFprZMjObYWYXmVnThuq3iJS2ZoXugJSPNm3asMsuu2jET0REJLfLgX2BJcDHwJ65Djaz44FHgeXAQ8BC4FjgFqAXcNLG7KyIlAeN+Em9SqVSCvxERERy+ynQGWgNnJfrQDNrDdwJrAYOdfez3f1SoBJ4BehnZqdu3O6KSDnQiJ/Uq1QqxWOPPcbixYtp3bp1obsjIiJSdNz9+fS/zTaYibIfsC1wv7uvTcnp7svN7HLgWaLg8cH67mcoQyeEsynmOidfuTIz1pdQf/MtLzWtWZxYXlGR/2e3fF+nXPcw3/sbOr4+3zsN8T4MynlDkrN65qIRP6lX6QQvb7zxRoF7IiIiUha+E2+fTNj3IrAUONjMNm24LolIKVLgJ/VKmT1FRETq1R7xdlb2DndfBXxININr14bslIiUHgV+Uq/atWvHdtttp8BPRESkfrSJt4sC+9PlW278rohIKVPgJ/XKzJTgRUREpEiY2UAzm2JmU+CzQndHRApIgZ/Uu1QqxVtvvcWKFSsK3RUREZFSlx7RaxPYny7/Mmmnu49x9+7u3j3KESMijZUCP6l3qVSKVatW8fbbbxe6KyIiIqXu3XjbOXuHmTUDdgFWAR80ZKdEpPRoOQepd5WVlUCU4CWd7EVERERq5TmgP3Ak8KesfYcArYAX3X2D02z2r1zNlH+sn8a/Z+/kFP6TJian/AewNsnnLAo8iThgQHL54rGTgm10CWSrr6pKLu851hPLc2XEHzI0eTmNwYMCdY28P7HcBpwRbCP0d/DQkhhDJiVf4Mie44NthJYcmBS4vSNHJ79+48NNBOW79EXoumvT/siRyeXt24fPCd2rQYPy61OuZR5C+7owM/n4wJu9emb4Zk2cmFzetWu4Xxrxk3q3++67s/nmm+s5PxERkbp7BPgcONXMuqcLzawF8Kv4298VomMiUlo04if1rkmTJuy7774K/ERERBKYWV+gb/ztDvH2IDO7L/735+5+CYC7LzazHxEFgC+Y2YPAQuA4oqUeHgEeapiei0gpU+AnG0UqleK+++5jzZo1NGmigWUREZEMlUD25Mdd+d9afHOAS9I73H2CmX0bGAacCLQA/gNcDNzm7snzEkVEMugTuWwUqVSKJUuW8J///KfQXRERESkq7n6Vu1uOr44J5/zL3Y9y963cvaW77+3ut7j76gJcgoiUIAV+slGkk7pouqeIiIiISOFpqqdsFN26dWOTTTZh2rRpnHLKKYXujoiISKM3dXrTxGycPXoEThg9OlhX375DEstDmThDhg4N75s3L7l81KDkzIiDRwcyI+bI6hnK3jlqdHK2T/o+k1gcygiZq/0hBFJSjh2b3Kcc97a6Z3Im0CGTklNShu5t8L0A+KLkLK8VXZIzhIYybuaS67VKEsoWm+s68m0jdK9y/HgwYkRyuXVNfhH79g0cH8ieC7kzl4ZoxE82iubNm9OtWzemT59e6K6IiIiIiDR6Cvxko6msrGTatGnomXMRERERkcJS4CcbTSqVYv78+XzyySeF7oqIiIiISKOmwE82GiV4EREREREpDgr8ZKPZd999AQV+IiIiIiKFpqyestG0bt2a3XffXYGfiIhIEWjVCrp1W7+8KjkhJIwaFayruiI5q2fI4MF5NxHMzDiT5MyIkyYlH58ri2P1yPuTdwSyd9KnT2LxaML5DEaNSM6GCYEL7N07sXjevMAFAlRUBM4Jn5KvwUOTM0yG3j+hjJv1KZQ9M9dr3rNncvmQ/N7SwUycEM5WG8rEGXrv5srcGfqZypUpt2hG/MxsRzO7x8zmmdkKM5ttZr8xs63yrGfr+LzZcT3z4np3rK+2zay5mf3czN4ws6VmttjM/mlmJ2+gbwPMbJKZLTGzRWb2gpkdk+P4pmb2UzObYWbLzGyhmT1hZgfX/I4UViqVUuAnIiIiIlJgRRH4mdluwFTgh8Ak4BbgA+BC4BUza1vDetoCr8TnvR/XMymud6qZ7VrXts2sOfAUcD3QGrgXGAfsBDxkZtcE+nYjcB/QDrgT+AOwN/BXM1svZjczAx4EbgaaA6OA8cAhwItmdnxN7kmhpVIpPvzwQ7788stCd0VEREREpNEqisAPuB3YDrjA3fu6+y/c/TtEQdgewPAa1jMC6Azc7O7fjevpSxTEbRe3U9e2BwGHEgWYe7n7IHc/D+hGFEBebmbdM0+IR+h+RhSM7uPuP3X3QcD+wELgRjPrmNXOqUA/4GWg0t0vdfezgcOA1cCdZrZFDe9LwaQTvGg9PxERERGRwil44BePuB0OzAZGZ+2+EvgaON3MNttAPZsDp8fHX5W1exQwBzgic9Svlm2nZzIPd/ev04XuvgT4FWDAT7LqOjfjnC8yzkm3uynRiGOm8+Lt5e6+POOcycBDwLZEgWFRU+AnIiIiIlJ4BQ/8iEawAJ529zWZO9z9K+BfQCvgwA3UcyDQEvhXfF5mPWuIpmdmtlfbtneItx8k9CFd9t2s8u/E2ycTzvl71jGYWQvgYGAp8FJNzilW22+/PTvssIOe8xMRERERKaBiyOq5R7ydFdj/HtGoXGfg2TrWQ1xPXdr+HOgE7ALMzDo+PZq4s5m1dPdl8WhhBbDE3ZNWMk/q125AU+ADd19Vw3OKlhK8iIiIFN5mmyVnNAxlz+xZEU6NOInk1Ig9K3JknkwQSEYJwMSJyeWhrIWhLIe52IAzEssHDUo+PpS907FgG1UDks+pqEjO3lkdKA+8TFEb1cnpUUOvbSjr5dix4TYCyUbzvu+52jijemRi+cy+ySk3J09Ormf8+HAboWsPZfucMCG5PNd1hDKahuoKZe/MlZU1lAk0l2IY8WsTbxcF9qfLt9wI9dTmnL/F22Fm1jJdGAd4mb+K0uc0VL+KViqV4u2332b58uUbPlhEREREROpdMQR+peZW4A2iqZhvmdkoMxsNvEX03F06KFsTOH+jM7OBZjbFzKZ89tlnherGWqlUitWrV/Pmm28WuisiIiIiIo1SMQR+6UCpTWB/uvzLjVBP3ufESVy+BYwEVgE/Ak4BXozLm8blCxuyX5ncfYy7d3f37ttuu22gioaTTvCi6Z4iIiIiIoVRDIHfu/E29Lxap3gbeg6vLvXUqm13X+LuQ929s7tv6u7buPsZRNk5NwfecPeV8bFfA9XA5mbWroZtvE+0ZMOuZpb0HGZN70lR2GWXXWjdurUCPxERERGRAimGwO/5eHu4ma3Tn3idul5E2S1f3UA9rwLLgF7Z69vF9R6e1V59tp2WfkL4j1nlz8XbIxPO+V7WMcTLN7xMlFH0/2pyTjFr0qQJlZWVCvxERERERAqk4IGfu78PPA10JFocPdPVwGbAA5lr5pnZnma2Z1Y9S4AH4uOvyqpncFz/U+7+QcY5ebcdt986+zrMrA9wGdFo3R1Zu38fb4eZ2VYZ56TbXQHcm3XO7+Ltr+LlHdLn9CCaWvoZ8Gh2P4pVKpVixowZrF69utBdERERERFpdIphOQeIFjx/GbjNzL5LtEzCAUTr7M0ChmUdn15GITt37lDgUOBiM6sEJgFdgOOB+awf3NWmbYB3zGwG8A6wHNgP6A18ChyfHSi6+8tmdjNwMTDDzB4BmhMFcFsD58eLuWd6EDiBaJH2aWb2V6BtfE5T4Efuvjihb0WpsrKSpUuX8t5777Hnnntu+AQRERFpEKEU85NGBNZTACYSyCUfWGohtARDaNkEgC5dksure1YlllflyuEf8PbbgTYCKf9HjUj+6BVasgFg/ITkpR6q+iafM35sqI31xh3WCi1FELqO0FIAXbsGmwgalbySBH37JpcPSV6ZAYAB85J39g30N3R9I0aE2wgtAZHve7RNKBMH4WU0Qv0KHR/62QQYPTq8L6TgI36wduStO3AfUdD1M6K17G4FDnT3BTWsZwFwEHAbsHtczwFEo2n7x+3UR9vjiNbmOwu4ENgZ+DWwl7u/Fejbz4AfEgWHA4mmhb4FHOvu6/3IuLsD3ycKFlcB5xMFgi8Ch7j74xu8IUVECV5ERERERAqnWEb8cPePiAKjmhwbXCXT3RcSBWMXboy24+MvBS6t6fEZ591HFGDW9PhVwC3xV0nr2rUrzZs3Z9q0aXz/+98vdHdERERERBqVohjxk/K3ySabsNdee2nET0RERESkABT4SYNJpVJMmzaNaBariIiIiIg0FAV+0mBSqRQLFizg448/LnRXREREREQalaJ5xk/KX2aCl5122qnAvREREWlcPvssv0yAowZVBPdNCNQTypgYylqYqz+hjJs9ByRn7wy1nUsoG+YQRgbOSL6QiorewTaC2TsD2T6Z9Exi8YQJ4TZCdVW0T267KjkxKmPHBpugy+T7E8vvX7uM9bpCmUMrwm+roFD2ztBrnquNefPyazuULDb0ns4llDm0ffv86wplCA21ARrxkwa0zz77YGZ6zk9EREREpIEp8JMGs/nmm9OpUyemT59e6K6IiIiIiDQqCvykQaUTvIiIiIiISMNR4CcNKpVKMWfOHBYuXFjoroiIiIiINBoK/KRBpRO8aLqniIiIiEjDUVZPaVCZmT2/853vFLg3IiIijUfHjnD11euXh7Iv5k4BOiqxdNCg5KNrk8lxwIDk8lA2w9DxuQyZlGd6y97JmTWrc2T1HD92cfKOQPZO+vRJLO7RI8c6yIMD/R0SaDrwmocyWAJUVCRn7wwJZdzMlcEylHEzVB7KrDlhQriNkFBdoevIlR00dI2hn4/Qj1rfvuE2QvvqNaunmW1lZl3NbNOs8h+a2eNm9kczCyTHlcZu2223paKiQs/5iYiIiIg0oNqM+I0ATgO2SxeY2fnAb4D0IiJ9zay7uwdWYJHGTAleREREREQaVm2e8esFPOvuyzLKLgGqgUOAk+Oyi+vYNylTqVSKd955h6VLlxa6KyIiIiIijUJtAr8K4MP0N2bWFdgJ+K27/9PdHwH+ShQEiqwnlUqxZs0a/v3vfxe6KyIiIiIijUJtAr+WwPKM73sBDkzMKHufKEAUWU9lZSWgzJ4iIiIiIg2lNs/4VQN7Znx/BLAYeCOjbCsgcyqoyFodO3Zkyy231HN+IiIiDajt/JmcMWr9/Hs9xianeLSuyZk7IZydcNSk5Px+9w9ObqNnjnSAoQyTgaSXtTKyZ3Ijo7okHz9vXvJ1BBJCAlA1oHVi+YQJyZlAQ9k7J022xHIAG5B8jo+9P7F8ZHVyhs5c2VfP6JucnXTipOTry5kUNiCUbbQqkHw11Eau91VI6JxQVs9Q4leAUYEfnVE9k18PSH49QhlsoXb3tzaB3/PAADMbTDTydxzwqLuvyThmN+CjWtQtjYCZUVlZqcBPRERERKSB1Gaq50hgCXArMIYo+LsqvdPMWgPfAl6uh/5JmUqlUsyYMYNVq1YVuisiIiIiImUv78DP3T8EugEXAhcAe7n7uxmH7A7cAdxXHx2U8pRKpVi+fDnvvvvuhg8WEREREZE6qc1UT9z9UyBx9qq7vw68XpdOSflLpVIATJs2jW7duhW4NyIiIiIi5a02Uz0TmVlbM6sysyPMrGl91Svlac8996RFixZ6zk9EREREpAHkHfiZ2Xlm9pqZbZ1Rtj/wDvAI8ATwspltVn/dlHLTrFkz9t57bwV+IiIiIiINoDZTPU8B3N0XZpTdQLSEw73A9sDRwLnATXXuoZStyspKHnnkEdwds3CKYhEREam7qUu7YJPXz5ffPnlVAXzEyGBdg6uHJJYn1Q/QI5DePpQqH3Knsk8ydGh+x0N4+YLqnoH1AwInVFWHl74ILRMwfkLgs8/g5HUCQks2ADjJdYXO6RFYf2Lw4GATwbUWQstShFRXh/fNm5dX00G5lqUItZ/v8ghDkn8EcrZvA5KXbWjfPv8+hZZUyaU2Uz07ATPS35jZNsC3gbvd/Rx3PxaYDPygFnVLI5JKpfjiiy+YO3duobsiIiIiIlLWahP4tQXmZ3zfK95mroD5EtChtp2SxiEzwYuIiIiIiGw8tQn8FgLbZHz/bWAN667b50CLOvRLGoF99tmHJk2aKPATEZFGI06Gd46ZjTez/5jZMjNbZGb/NLOzzSzxs5mZHWxmT5jZwvicGWZ2kRLqiUhN1SbwmwkcG//i2hI4FZjs7oszjukIfFr37kk5a9WqFXvssYcCPxERaUxOAu4EDgBeA34DPArsBdwF/NmyHnw3s+OBF4FDiGZYjQKaA7cADzZUx0WktNUm8LsVaAd8DHxElMzl9qxjDgTeqFvXpDFIpVIK/EREpDGZBRwH7Oju/d19iLufBexJ9LnqROCE9MFm1pooUFwNHOruZ7v7pUAl8ArQz8xObeBrEJESlHdWT3f/i5mdCwyMi8a5+x/S+83sUGBz4Kn66KCUt1QqxR//+Ec+//xzttlmmw2fICIiUsLc/blA+adm9ntgOHAo0SggQD9gW+B+d5+ScfxyM7sceBY4jxqM/HXrBg8/vH55KBumDQ2nLQxlhXz77eTyUBuhjJe5zsm3T7mEskUOmTQ+sTyUdTJX26EskhXtA1k6A7fdx94fbCOUvTOU7ZOJi5L71KV1sA1GJmfvHJ98q4KZLXNlcg3dx0lVyRlmZ47NkVozoDbZX5OE3gu59oVew5HVydk+h/SdGWyjoneXcAcCarOcA+4+BhgT2PcC0dIOIhuUmeClT58+Be6NiIhIQa2Mt6syyr4Tb59MOP5FYClwsJlt6u4rNmbnRKS01Waqp0i9qaysBJTZU0REGjczawak/+yfGeTtEW9nZZ/j7quAD4n+kL/rRu2giJS8Wo34AZjZgcA5QArYElgETAXudfeXc5wqslbbtm3ZaaedmD59eqG7IiIiUkjXESV4ecLdMx+XaRNvk+fm/a98y6SdZjaQ+PGcdu12rnsvRaRk1WrEz8x+BfwLOIso8NuF6CHjs4GXzGxEfXVQyp8SvIiISGNmZhcAPwPeAU6vz7rdfYy7d3f37ltvvW19Vi0iJSbvwM/MTgKGAnOJRvx2BVrG23Pi8svM7OR67KeUsVQqxbvvvsvXX39d6K6IiIg0KDMbTJQx/W3gMHdfmHVIekSvDcnS5V/Wf+9EpJzUZqrn+cB/gR7u/nlG+WzgHjP7C/AmMAj4c517KGUvlUrh7syYMYODDjqo0N0RERFpEGZ2EdFafG8C33X3+QmHvQt0BzoTPVKTeX4zollXq4APNtTe/PkwevT65aHMlrmEMjOGyquqksvPaD8x2EbPnvllkRw7Nrm8NYuTdwAjRydnscyVsTFJKHMnhO9v6J6Ejg9lfoQcWUUD2Ttpk/x3hKpBgUyjhO976DpCx+cSev8snpicvTOU2LJ6ZDgD6ogR4fuYJHR9Q/JPKBp8cYf0bZ98/NCEH9jY2LHJNzhXrsTaTPXcF3gkK+hbKy5/mGjqZ42Z2Y5mdo+ZzTOzFWY228x+Y2Z5ZQg1s63j82bH9cyL692xvto2s6Zm1t/MXjKzT81sqZnNMrN7zaxbwvEvmJlv4OvurHOu2sDxR+ZzX4pZZmZPERGRxsDMLiMK+qYTjfQlBX0A6eUfkv7fPwRoBbysjJ4isiG1GfFrRpQ6OJel+dRtZrsBLwPbAY8TzXHvCVwIHGlmvdx9QQ3qaRvX05noF+WDRAui/hA42swOcvcPss6pTdt/BE4mWsT+MeArYG9gAPADM/te1jo99wEvBLp9PrA18PfA/rFEo6nZ/hM4vuTstNNObL311gr8RESkUTCzXwLXEI3gHZ4wvTPTI8D1wKlm9tv0Wn5m1gL4VXzM7zZmf0WkPNQm8HsfOMbMhrj7muydZtYEOCo+rqZuJwq8LnD332bUdTPwU6LFTM+tQT0jiIK+m939Zxn1XEA0f/521v+LWV5tm1kPoqDvLaCnuy/N2PdD4B7gcv73Fzrc/b6kzprZHsCVRFNnHw9c033x2ohly8yU4EVERBoFMxtAFPStBl4CLjBbb5Ht2enPDu6+2Mx+RBQAvmBmDwILgeOIlnp4BHioYXovIqWsNlM9/wh0AR43s06ZO+LRs0eArvFxGxSfczjRqFb2RNYrga+B081ssw3UszlRJqyvgauydo8C5gBHmNmuGefUpu30+c9mBn2xdPBW07RZA+Ptve6+MueRZS6VSvHvf/+blSsb9W0QEZHyt0u8bQpcRPR5I/vrzMwT3H0C8G2iBdtPJJottBK4GDjV3cMPZomIxGoT+N1M9IvnaGCmmc01s9fMbA7RA8h9iZZ6uLmG9R0Wb5/OHkF096/iuloBB26gngOJsov+Kz4vs541QHpNnMMydtWm7bfi7XfMrGVWH46Jt+EnlWNmtinRQq0O3Jnj0G+Z2SVmdpmZnWJm22yo7lJUWVnJN998w8yZMwvdFRERkY3G3a9yd9vA16EJ5/3L3Y9y963cvaW77+3ut7j76gJchoiUoLynerr7N2bWB7iEaB2/3YB04pT3iaY63pjHCNYe8XZWYP97RKNynYFn61gPcT21btvd3zSzW4imgb5jZv+P6Bm/bkTTSB8kmuq5IScA2wDPZD93mOXarO9XmNkNwBXl9Be+dIKX6dOns88++xS4NyIiIo1Hz57J5bmyfYb+TjthQnJ5KAPipKrkzJ0Ao/om/x19wITkc5IylgJUVCRn7oRw5slglsyAUEZRgK5d8zsn1KeKinAbgwcHzumSfO2h7J2jRq837fh/nnkmuXzAgMTi8SSnOh00KNzEqBHJGVhD1xF679K3b7CNLqNHJpZXVye/SQOXh/cNpPsEKiYlv4iDGZXcduC9G8psCzA0R/bOkFot4O7uK919pLt3AloDOwGt3b2Tu48EmppZ+KdsXel8soF8s2vLt9wI9dSqbXe/mOi5v22BnwCXEY32vQGMdfeaLEiXnuY5JrD/DaLAOr1OYgfgR0Tr9FxO9OxhIjMbaGZTzGzKZ599VoOuFN4ee+xBy5Yt9ZyfiIiIiMhGUKvAL5O7L3H3andfklH8O6IHj8uORW4jeibwGqKgdwvg/4imbf7dzHL8LQPiZyMPJUdSF3cf7+73uvuH7r7c3ee6+11EiXNWApeEpn26+xh37+7u3bfdtqaPGxZW06ZN2WeffRT4iYiIiIhsBHUO/HLIMVa8jvSoWvJKkv8r/3Ij1FObcwYQPVR9m7tf5+4fx8HvP4FjgWXAdXGymZBaJ3Vx99eBScAmQFmtdp5KpZg+fTplNINVRERERKQobMzAr6bejbedA/vTmUNDz+HVpZ7anJNO4PJ89sHu/inROoCb87/nB9dhZs2JgscNJXXJJT1/M2em01KTSqVYtGgRH374YaG7IiIiIiJSVooh8EsHUIfHawCuZWZbAL2IFoR/dQP1vEo02tYrPi+zniZESVoy26tt25vG29AcynT5N4H9VfExEzeQ1CWRmW0C7Bd/m/f5xSyd4EXTPUVERERE6lfBAz93fx94GugIZD8bdzXRqNYDmQlTzGxPM9szq54lwAPx8Vdl1TM4rv+pzGCrNm0TLbYKcLGZrTNF1MzOJcpw+inwduCS09M87wjsx8y2iBd3zy5vDvwG2JloZHFKqI5StNdee9G0aVMFfiIiIiIi9Szv5Rw2kp8ALwO3mdl3gZnAAUTr7M0ChmUdn04inP0c4VCipCkXm1kl0bNwXYDjgfmsH9zVpu3bgf7APsAsM/sL0TOA+wHfAVYDg5LW1TGz3eN6/wv8JelGxNoSrZE4Je7PJ0SjhIcRLfz6OfD97LUHS13Lli3Zc889FfiJiIgUgdASAQC9A1nmQ+fMm5dcHlqCAaCiIrmR9u2Tjx86NFxXvnxR8rICg4cmJ60P3Y9cuky+P7G8ouKMxPIz+ib3CQivvTEyuWOhJSOCSzYA9AmsHxA4Z2Jg+Ylcr3nP3sn3N7SURWj5kJnVORYX6Ju8bEN18koLwffbxEGhmwhVOZbeSGw7eeWL8OtUSwUf8YO1I2/dgfuIgq6fEa0PeCtwoLsvqGE9C4gSntwG7B7XcwBwL7B/3E6d2o5HFnsBVxIFZD8ALiIKMB8GDnb3xwJd/BFRsLqhpC4LgVHAGuAI4GLgFKL1Aq8Hurn79Bznl6x0ghcREREREak/NRrxM7P1Rq/qm7t/BPywhscGM4a6+0Lgwvir3tuOj19CtJTDNTU9Jz7vMqI1/zZ03GLggnzqLhepVIo//OEPzJ8/n+22267Q3RERERERKQs1HfGzWnyJ5E0JXkRERERE6l+NAj93b1KLr6Ybu/NSfiorKwEFfiIiIiIi9akonvETSdtqq63o2LGjAj8RERERkXpULFk9RdZKpVIK/EREROrZZptBz541Pz5XRsGqquTyUAbEHj2Sy0PZGgH69k0uD2XvHJSUu30DbYSyKVZ0Sc4KGbruXBlQRwWyRd5PcvbOkImTwpkqJ0zIL3tn6DoYMCDcgVDGz0C2z6F9PbE81+sxaWJy5tLQ6xF6zbtUTww3EngDVVUlZ0YN3cPeQ8M/TKMrkusK/fyF3oe53leTJ4f3hWjET4pOKpXivffe46uvvip0V0REREREyoICPyk66QQvb7zxRoF7IiIiIiJSHhT4SdFRghcRERERkfqlwE+KTkVFBdtss40WchcRERERqScK/KTomJkSvIiIiIiI1CNzT864I+Whe/fuPmXKlEJ3I2+XXXYZt9xyC0uWLKF58+aF7o6IiNSRmU119+6F7kdjttVW3f3QQ2v+mWBScmJCAObNSy4PZeIMCWUzhHAGxFBWyFxZSPNtf+TI5PJcSS9DQvckdB2jR+dXTy6h68t130MmBhJlhrKsjp9gyTtGjAi2Mbh6SGJ5bfobEqpr0ojABU6YkFjcc1IgXSswieQ3r01O/qEKZb3Nlbnz7beTy7t2Df+u1YifFKVUKsXKlSt5O/SuFhERERGRGlPgJ0UpndlT0z1FREREROpOgZ8UpU6dOrHZZpsp8BMRERERqQcK/KQoNWnShH333VeBn4iIiIhIPVDgJ0WrsrKS6dOns2bNmkJ3RURERESkpDUrdAdEQlKpFLfffjvvv/8+nTp1KnR3REREStqWW0JV1frlowLJCUOZOyGcYTKUsDGUnTBX5tD6ym5Zm8yh+Ro7NrxvSHKiymBWz/bt828/dI25skImGTQovC/0eoSuI/hmCKUBBUY9k5zecvCE3onlodcvdM9zCl1g0g8NMDGcnBS6JL8goeydtXkfdqlYnPc5GvGTopVO8KKF3EVERERE6kaBnxStvfbai2bNmuk5PxERERGROlLgJ0Vr0003pWvXrgr8RERERETqSIGfFLVUKqXAT0RERESkjhT4SVFLpVL897//5ZNPPil0V0RERERESpYCPylq6QQvGvUTEREREak9LecgRW3fffcFosDvqKOOKnBvREREStf8+clLN4RSyedaCiC0fMGAAcnloTZyLbUQWloglHU/tHpArjbGj8//nCRnVI8M7hswL7+1BULLaORa+iJ0Tmj5gNBrO2pEeImAnr1bJ/drYvI5g4cmX3doyQYA+vRJPmfRouTjA2+GSVXhex56Xy2uSH4zhN5vQ4YODraxeGbyG2hym+TjQ++3XEuqVA1Ifj1y0YifFLU2bdqw6667asRPRERERKQOFPhJ0UulUlrLT0RERESkDhT4SdFLpVK8//77LAoN84uIiIiISE4K/KTopRO8vPHGGwXuiYiIiIhIaVLgJ0VPmT1FREREROpGWT2l6LVr147tt99egZ+IiEgdrFyZnD0wV7bIkFD2zlBdFRXJ5bmyZ06YkFeXanUd9WVm33AWyb6BfuWbybGqKtx+6NonVSVnG108Mbm/FV3CmSJDr2HonFAm18ETegfbCGbvbBNIh/nMM4nFIwKZOwGGDk0uD70eoevuOSkhRW6sKpAJNJQNNynbLsDgcOLQYEbaXDTiJyUhlUop8BMRERERqSUFflISUqkUb7/9NitWrCh0V0RERERESo4CPykJlZWVrFq1ijfffLPQXRERERERKTkK/KQkKMGLiIiIiEjtKfCTkrDbbruxxRZbaCF3EREREZFaKJqsnma2I3ANcCTQFvgEmABc7e5f5FHP1sAVQF+gHbAAeBK4wt0/ro+2zawpcCpwLtAJaA18DPwLuNHd38o6/lDg+Rzdvt7dfxFo5wLgh3E7y4BXgV+5+8s56is7TZo0Yd999y2bEb9x48YxbNgw5s6dy84778zw4cPp379/obslIiJlbOXKcMbIJD16hPeFMh2G6q9Nxs2+fZPLQ9k+Q1kkc2UOHZmc9DKYtXTEiOTyyZPDbYTaD50Tuu+jA5kic5k5Njl7Z+8uyceH7iGE7/ugQAbN0HXnaiN4kYHsnfTpk1g8oK8Hmwi1n+/7J5ShE8Kv7ZBA8tfQz02u925tFEXgZ2a7AS8D2wGPA+8APYELgSPNrJe7L6hBPW3jejoDzwEPAnsSBU5Hm9lB7v5BPbT9R+BkomDvMeArYG9gAPADM/ueuz+X0MV/AC8klP8z4Vos7n8/4F1gFLA1cArwopmd6O6P57whZSaVSnHPPfewevVqmjZtWuju1Nq4ceMYOHAgS5cuBWDOnDkMHDgQQMGfiIiIiGwURRH4AbcTBV4XuPtv04VmdjPwU2A40ejahowgCvpudvefZdRzAXBr3M6RdWnbzHoQBX1vAT3dfWnGvh8C9wCXEwWe2V5w96tqcB0QjSj2IwpKv+vuy+M2fk8UKN5pZs+5+1c1rK/kpVIpvv76a/7zn/+wxx57FLo7tTZs2LC1QV/a0qVLGTZsmAI/EREREdkoCv6MXzzidjgwG8ge370S+Bo43cw220A9mwOnx8dflbV7FDAHOMLMdq1j2+nzn80M+mLpEbhtc/W1hs6Lt5engz4Ad58MPBS30a8e2ikZ5ZLgZe7cuXmVi4hIeTGz683sWTP7yMyWmdlCM5tmZlfGs5eSzjnYzJ6Ij11mZjPM7KL4sRARkQ0qeOAHHBZvn3b3NZk74tGsfwGtgAM3UM+BQEvgX9mjYHG9T2W1V9u208/vfcfMWmb14Zh4OzHQx93NbLCZDTWzs8ysU9JBZtYCOBhYCryUcMjf030ItFOWunbtyiabbFLygd92222XWL7zzjs3cE9ERKRAfgpsBjxDNCNpHLCK6A/XM8xsp8yDzex44EXgEGA80R+0mwO3ED0WIiKyQcUw1TM9Z29WYP97RKNynYFn61gPcT21btvd3zSzW4h+ab9jZv+P6Bm/bkTTSB8kmuqZpH/8tZaZPQr8KCuJzG5AU+ADd19Vw2spe82bN6dbt24lHfh98MEHLF26FDPDfd0Hj6uqqgrUKxERaWCtM2fzpJnZcGAoMAT4SVzWGrgTWA0c6u5T4vJfEj1W0s/MTnV3BYAiklMxBH5t4u2iwP50+ZYboZ5ate3uF5vZu0R/aftJxq6pwFh3/zqrns+AXwB/I5pW2gLoTvRM4onADmZ2SMaoY53uiZkNBAZC+Y0ipVIp/vrXv+LuRPlvSseXX37J0UcfTbNmzfj1r3/NqFGjmDt3LjvuuCPNmjXjnnvu4Sc/+QmdOiUOBIuISJlICvpifyYK/DL/I+hH9HjH/emgL12HmV1O9Ifp86jByF+rVtCt2/rloQyEubIvhpIv5puJM5SsEXJnykwSyjQaKgdo3z65PJRZM5Rlcfz4cBuhTKChfuWbtTRXXSHVI+9P3hF6AYGZ1a0Ty7tUJ090qxrdO7E8lNkSYFJV8s4Rgcyhoeyd4yfk+IxYnfzi2uTk1LOh90KurLejRgWanrk4sbxqQPK9HT9iZrCNilBq1hyKYapnSbHIbUTPBF4D7ARsAfwf4MDfzWydt6e7v+Xu17v7m+6+xN0/d/cngUOBD4FewLH11Ud3H+Pu3d29+7bb1sfjhsUjlUrx+eefU13f+W03sm+++YYTTzyR999/n8cee4xLLrmE2bNns2bNGubOncvzzz9Ps2bNOPHEE9dL/CIiIo1G+rPAjIyy9GMdTyYc/yLRYyEHm9mmG7NjIlL6iiHwS49etQnsT5d/uRHqqc05A4Dzgdvc/Tp3/zgO5v5J9At7GXBdnGwmJ3dfTLQ0BETz9uvSr0YhneCllBZyd3fOO+88nnvuOe68804OPfTQ9Y7p0KED48aN48033+Tcc89dbxqoiIiUHzO7xMyuMrNbzOwl4FqioO+6jMOCj6XEj4N8SDSDa9fs/SIimYoh8Hs33oaeV0tPdwg9h1eXempzTjqBy3oLsrv7p0TrAG7O/35Rb8hn8TYzc+j7RHP5dzWzpOm4Nb0nZWfffffFzErqOb/rr7+ee+65h8svv5wBoRVhgSOPPJIrr7ySBx54gDvuuKMBeygiIgVyCVEW8YuAbxGN6h3u7p9lHFPnxz/MbIqZTVm16rOkQ0SkkSiGwC8dQB1uZuv0x8y2IJoGuRR4dQP1vEo02tYrPi+zniZESVoy26tt2+mpFKE5lOnybzbQ37R0xtC1C8vHc/9fJsoo+n8J53wv3iatFVjWtthiC3bfffeSCfwefvhhhgwZwqmnnso111yzweN/+ctf8r3vfY8LL7yQSZOS55qLiEh5cPcd3N2AHYATiEbtppnZfvXYxtrHP5o1K6/HP0QkPwUP/Nz9feBpoCOQ/ejm1UQjYQ9kJkwxsz3NbM+sepYAD8THX5VVz+C4/qfcPTPAyrtt/re8wsVmts5UTDM7F9gR+BR4O6O8+3oXHpWfBpxCFCT+OWv37+Ltr+LlHdLn9IjP+Qx4NKnecpdKpUoi8Hv11Vc5/fTTOfjgg7n33ntrlIymSZMmPPDAA7Rr145+/frx+eefN0BPRUSkkNz9v+4+nuiP1G2BzMwbevxDROpFMWT1hCgz5svAbWb2XWAmcADROnuzgGFZx6dT3GR/kh5KlDDlYjOrBCYBXYDjgfmsH9zVpu3biZZk2AeYZWZ/Ifplux/RA9irgUHuvjrjnEfMbBUwBfiYKKtnD6An0bo9P3b32VntPEj0179+RH/9+yvRfwanEC318KP4GcFGJ5VK8ec//5kvvviCrbbaqtDdSfThhx9y3HHHUVFRwYQJE2jRosWGT4q1bduWRx99lIMPPpgf/OAH/P3vf6dpU63PKyJS7tx9jpm9DVSa2Tbu/jnRYyndiR5LmZp5fPw4yC5EnyU+yK5PRCRTUQR+7v5+PCp2DdFaeEcBnxAtanp11hp3uepZYGYHEc2X70s0TXIBcC9whbt/XNe23X2JmfUCLiYKzH5AtIjqZ8DDwI3unj1H73dAb6Kpo9sQBazVwH3Ab9z9jYR+uZl9nygoPYsoocxyogxev3L3l2tyT8pRZWUlECV4OeywwwrbmQTpZRtWrlzJ3/72N2qTWXX//fdn1KhRDBw4kKuvvrpG00RFRKQspBcYSP8B+TmiPzgfCfwp69hDiB4LedHdV2yo4ubNk9P+h5YCyLV8QCi5dr7LCuQSSpcfWjohV3r9kFB/Q9cXuie5ko2HlqWYNy98Tr5C7Q8dmlw+YsQZieVdRo8MN9I3sA5DoJFqkpdzyGVQYNmG0HUE36OBJRuA4AsSWtoj9Pr1nhdYEgMYUJ18fydOSl62IfR0z0zCSzbU5v1TFIEfgLt/BPywhscG58y5+0Lgwvir3tuOj19CFCjW6NO4u18PXF/T+jPOW0W0VuAt+Z5bztKZPadNm1Z0gd/KlSvp168f7733Hk8//TR77rnnhk8KOOecc3jllVe49tprOeCAAzj66KPrsaciIlIIZtYZ+K+7L8oqb0KU1XM74OWMPzw/QvQZ4lQz+23GAu4tgF/Fx/wOEZENKJrAT6Smtt9+e9q1a1d0z/mll2149tlnuffee+sclJoZo0ePZtq0aZx22mm8/vrr7LLLLvXUWxERKZCjgJFm9k+ipRgWANsD3yZK7vIp8KP0we6+2Mx+RBQAvmBmDwILgeOIMog/AjzUoFcgIiWp4MldRGojlUoV3Vp+v/71r7n77rsZNmwYZ555Zr3U2bJlSx59NMrhc+KJJ7Js2bJ6qVdERApmInA3URbwE4BLgROJgrmrgW7u/nbmCe4+gSgwfDE+9nxgJdFjJ6e6Fn8VkRrQiJ+UpFQqxVNPPcWyZcto2bJlobvDI488wi9+8QtOOeWUen8eb9ddd+WBBx7g2GOP5fzzz+euu+6q1/pFRKThuPubRNnG8z3vX0SjhSIitaIRPylJqVSK1atX8+abbxa6K+ss23DffffRpEn9/1gdc8wxDBs2jLvvvpu777673usXERERkfKmET8pSZkJXnrUJo1XPZk9ezbHH3887du3z3vZhnxdffXVvPbaawwaNIhUKsV++9Xb+r4iItIIVFTAiBHrl4cyQvbpE64rlAExlJVxVM/kDIg9hyZnP4RwNsVQ2127BqsKCvU3ZEggsWWuDKihjJQhoY81udoYPTq/NkKqqwMXCFSPSi6vqkpOSTmp78TkE3J0dnFFcsrWfLOs2uRAmkzC75/qeYHckX37JhbP7BFILwtUVyR3rOfQ5H4NDswBGDAg2ATetyqx3CaEz9GIn5SkXXbZhTZt2hQ0wUt62YZvvvmm1ss25KNp06b88Y9/ZNttt+XEE09k4cKFG7U9ERERESkfCvykJJkZlZWVBQv80ss2zJo1i8cee6xOyzbkY9ttt+WRRx6hurqa008/nTVr1jRIuyIiIiJS2hT4ScmqrKxkxowZrF69esMH1yN35yc/+QnPPvssd955Z4OvJXjAAQfwm9/8hieeeILhw4c3aNsiIiIiUpoU+EnJSqVSLFu2jHfffbdB273hhhu46667GDp0aL0t25Cv8847j/79+3PllVfy9NNPF6QPIiIiIlI6FPhJycpM8NJQHnnkES677DJOOeUUrr322gZrN5uZcccdd9CtWzd+8IMfMGfOnIL1RURERESKn2nNz/LWvXt3nzJlSqG7sVGsXLmSLbbYgvPPP58bbrhho7f32muvceihh5JKpXjuuec2agbPmpo1axY9evRgjz324KWXXmLTTTctdJdERBKZ2VR3717ofjRmm23W3bt1W/8zQSgzYm0yRQYSIDJ2bHJ5rqyFIaH+jg8kWayoCNc1KZD8cd68/PqUSyhzaKi/9dl2SG1ej1A2zJDqqkCqyhzpSUdWJ2d5DWX1DL22oXsL4WyxoSyZTJiQWFzVNxxDjSe5ropJyR2bOTO5nlyvx/iq5Ey5NmBA8HetRvykZG2yySbstddeDTLiN3v2bI477jjatWvH448/XhRBH0Dnzp257777mDx5MhdddFGhuyMiIiIiRUqBn5S0VCrFtGnT2Jgj1w29bEO+qqqquPTSS/n973/P/fcn//VHRERERBo3BX5S0lKpFAsXLuSjjz7aKPWvXLmSk046iVmzZvHoo4/SpUuXjdJOXY0YMYJvf/vbnHvuucyYMaPQ3RERERGRIqPAT0raxkzw4u4MGjSIiRMnMmbMGL7zne/Uexv1pVmzZjz44INsueWWnHDCCXz55ZeF7pKIiIiIFBEFflLS9t57b8xsowR+N954I3feeSdDhgzhhz/8Yb3XX9922GEHHn74YebMmcOZZ56pxd1FREREZK1mhe6ASF1svvnmdO7cud4Dv0cffZSf//znnHzyyfzqV7+q17o3pl69enHDDTfw05/+lBtuuIHLLrus0F1qMOPGjWPYsGHMnTuXnXfemeHDh9O/f/9Cd0tEpGhstx0MDiRaTJIrM2IoK2Qoy2IoQ2iujJuh5I+hTIcjRoTrCsm3v/lmLQVo0ya5vEeP5PJQVs9cbQwZkl9doeODmS2BiYOS3xC9hya/UD0DKVMn5nidhgxNfoP2nDQqsTx0T0L3FqD3vOR8CDN7JF/f0EA94ydYsI37xybnngjlM+3dO7k858/HqOQMqBBOBaoRPyl56QQv9WXSpEmcdtppHHjggdx33300aVJaPyYXXnghJ598MkOHDuW5554rdHcaxLhx4xg4cCBz5szB3ZkzZw4DBw5k3Lhxhe6aiIiISFEorU+0IglSqRQfffQRCxYsqHNdc+bMWWfZhpYtW9ZDDxuWmXHXXXfRuXNnTj31VKpDi9+UkWHDhrF06dJ1ypYuXcqwYcMK1CMRERGR4qLAT0peOsHL9OnT61TPokWLOProo1m+fDl/+9vf2G677eqhd4WxxRZb8Nhjj7F06VJOOukkvvnmm0J3aaOaO3dusHxjLvUhIiIiUioU+EnJq4/MnullG959910ee+yxol22IR9dunThnnvu4ZVXXuHSSy8tdHc2irfeeotjjjkmGNy5O3vvvTe33HILn3/+eQP3TkRERKR4KPCTkrfNNtuw44471jrwc3cGDx7MM888wx133FHUyzbk6+STT+aiiy7itttu409/+lOhu1Nv5s2bx49+9CP22WcfXnrpJU455ZT1puW2bNmSs88+m80335yLL76Y9u3bc9JJJ/HUU0+xevXqAvVcREREpDAU+ElZqEuCl5tuuokxY8YwZMgQzjrrrHruWeH9+te/plevXpxzzjm89dZbhe5OnSxevJhf/vKX7L777owdO5YLLriA999/nwcffJA777yTDh06YGZ06NCBO++8k7vuuotXX32Vf//73wwePJjnn3+eI488kl122YUrr7yS2bNnF/qSRERERBqE6fmX8ta9e3efMmVKobux0V155ZX86le/4quvvqJVq1Y1Pu+xxx6jX79+9OvXjwcffLDkMnjW1Lx589hvv/3YcsstmTRpEq1bty50l/KycuVK7rzzTq666io+++wzTj31VIYPH86uu+6aVz0rVqzgL3/5C3fffTdPP/00AL179+bss8+mb9++bLrpphuj+yICmNlUd+9e6H40Zttt191PPnn9zwSBrPtMnhyuq3375PLQchGh9PqhZRNy9Su0zEPo+FxC6fKrAqsaDA3k9g8t8wCQb4610PG5UvuHhF7D0OuXS+iehPo7vjrwQuW4IYtnJu/LdzmQUcmrP+Rsvroi0N9AI/dXhdc7OWNA8lIPFe2T467Qvc21pEr1xJmJ5da1a/B3bXl+ypVGp7KykjVr1jBjxowanzN58mROO+00DjjgAMaOHVu2QR9A+/bteeihh/jPf/7D2WefXTIJT9ydxx57jG7dujFo0CC6dOnCa6+9xp/+9Ke8gz6ATTfdlJNOOoknn3yS2bNnc+WVVzJr1ixOPfVU2rdvz4UXXpjXe6jcjRs3jo4dO9KkSRM6duyo5TFERERKWPl+0pVGJd8EL3PmzOHYY49l++23L9llG/L17W9/m5EjR/LII49wyy23FLo7G/Tyyy/Tq1cvTjzxRJo1a8Zf//pXXnjhBXqG/tSbp5133pkrr7ySDz74gKeffpo+ffrw+9//nn333ZcePXrw+9//nkWLFtVLW6VIayOKiIiUFwV+UhY6dOjAVlttVaPAb9GiRRxzzDEsX76cJ554oqSXbcjXJZdcwgknnMDPf/5zXnrppUJ3J9GsWbM48cQT6dWrFx9++CFjxoxhxowZHHPMMZglT52oiyZNmtCnTx8efPBB5s2bx6233sqKFSs477zzaNeuHQMGDODFF18smVHS+jJkyJDEtRGHDBlSoB6VPo2giohIITUrdAdE6oOZUVlZucG1/FauXMnJJ5/MO++8w5NPPlkWyzbkw8y455576NGjByeffDKvv/467dq1K3S3APjvf//LNddcwx133EHLli255ppruPjii9lss80arA9t27blggsu4Pzzz2fKlCncfffd/OlPf+L++++nU6dOnHXWWQwYMKBo7ll9+eKLL3j99dd5/fXXmTp1KlOnTuWjjz5KPPajjz5ihx12oEOHDut8dezYce2/S+0Z0oaQHkFNB9PpEVSA/v37F7JrIiLSSCjwk7KRSqW4/fbbWbVqFc2arf/WdnfOP/98nn76ae666y6++93vFqCXhdemTRsee+wxDjjgAE455RSeffZZNtlkk4L15+uvv+bmm2/m17/+NcuWLePHP/4xV1xxBdtvv33B+mRm9OjRgx49enDzzTfzyCOPcPfddzNkyBAuv/xyjj76aM4++2yOOuqoxPdaMVuwYME6Ad7rr7/OBx98sHZ/hw4d2G+//fjss88Sp7q2adOGY489ljlz5jB9+nT+8pe/sGLFinWO2XLLLdcLBjO/b9u27UYZvS1WK1as4LLLLkscQb300kvp2bMnrVq1YrPNNqNVq1ZssskmBb8/48aNY9iwYcydO5edd96Z4cOHK0AVESlxyupZ5hpLVk+AP/zhD5x++un8+9//Zq+99lpv/0033cQll1zCL37xC0aOHFmAHhaXcePGcdppp/Gzn/2MG2+8scHbX7VqFffddx9XXHEFn3zyCSeccAIjRoxgjz32aPC+1NSsWbO45557GDt2LJ9++ik77LADAwYM4Oyzz6ZTp06F7t565s+fv16QN2fOnLX7d911V/bbbz/2339/9t9/f1KpFNtssw2w/ggVQKtWrRgzZsw6AcCaNWuYP38+c+bMYc6cOcyePXvtv9NfX3311Tr9atWqVXC0sEOHDrRr1y6vZEsNEaS4O8uWLWPhwoXrfC1YsGC9suyv7IBvQ5o2bbo2CExvM/+dq6wmx7ds2ZKmTZsG26/pa58vZfUsvO6dO/uU229fr7xiQO/E4+fNC9cVygpZXZWc1rOqOjnN4vie4f+PK0YlTy0PPeo9YkSwqqAuhDIjJs8ICl13rnsV6lcoQ+igQcnlo3reH2zDBpyRWO5jA+cEUqAOJkc6zIBQZs3Q9YUyvEI4C+nYscnloacPqmcuDrYxcVLyrJRQf0NZQHOlHAhlmK2eF/ij3ttvJ5d37RpsY+SI5Bhu6NDw79rS+lO1SA6ZCV6yA7/x48dz6aWXctJJJzF8+PBCdK/o9O/fn1deeYWbbrqJAw88kH79+jVIu+7OE088wWWXXcZbb73FQQcdxMMPP0yvXr0apP266Ny5M9dddx3XXnstf//737n77ru58cYbuf766znkkEM4++yz6devX15LitSXTz75ZL0g7+OPP167v1OnThx44IEMGjSI/fbbj/3224+tttoqWF/6A/6GAqomTZqwww47sMMOO3DAAQesV4+788UXX6wXDKYDxMmTJ7NgwYJ1ztlkk03Yeeedg8HhjjvuuHaUOt8plO7OkiVLcgZqoWAue2QzU/PmzWnbti1bb701W2+9Nbvssgv777//2u9vuukmFi5cuN552267Lbfccgtff/01S5cuXbvN/Hfm9osvvlivLFe/Qlq0aBEMEF966SWWLVu2zvFLly5l2LBhGvUTESlhCvykbOyxxx60aNGCadOmcfrpp68tnzx5Mv3796dnz55lv2xDvm666SamTJnCWWedxd57773RR9umTJnCpZdeygsvvECnTp149NFHqaqqKvi0tnxtsskmHHfccRx33HF88sknjB07lnvuuYcBAwZw/vnn8/3vf5+zzz6b7t271/u1uTvz5s1bJ8CbOnUqn3zyCRBNU+3cuTOHHHII+++/P/vttx+pVIo2bdrk3Vb//v3r/EHfzNYGP+k/zmRbsmQJc+fOTRwxfOqpp5iX9af0Jk2a0L59ezp27Mjrr7+eOIXyvPPO45lnnkkM5FatWhXsb6tWrdb2d+utt2aPPfZY5/ukr7Zt29KyZcucr3WHDh0SR9FuueWWOt/j1atXrw0WkwLGUBCZtG/RokXrBX1pc+fOrVM/RUSksBT4Sdlo1qwZe++99zqZPefMmcNxxx3XqJZtyMemm27Kww8/zH777ccJJ5zAa6+9xuabb17v7Xz44YcMHTqUBx98kG233ZZRo0YxcODAgj5bWF/atWvHL37xCy677DJeeukl7r77bu6//37uuOMO9t57b8455xz69+9P27Zt867b3fnoo4/WCfCmTp3K/PnzgSgA2nPPPendu/faIK+yspItttiivi9zo9p8883p2rUrXQNTWlasWMFHH32UOGIYmkb51Vdf8cILL6wNzvbee+8NBnBbbbXVRvsdUdMR1Npo2rQpW2yxRb297h07dlxnSnDazjvvXC/1i4hIYSjwk7KSSqX485//jLuzePFijjnmGJYtW8azzz5b0GQhxWynnXbiT3/6E0ccccTaddrqa5RqwYIFDB8+nFGjRtGsWTMuv/xyLr300rLM+mhmHHLIIRxyyCHcdtttPPjgg9x9991ceOGFXHrppVRVVXH22Wfz3//+l8svv3y9D//uzuzZs9cJ8l5//XU+//xzIPpw37VrV4466qi1z+Xtu+++DZr1tFA23XRTdt99d3bffff19oWClA4dOjB79uwG6F3N1ccIakMYPnx44uikpsmLiJS2ogn8zGxH4BrgSKAt8AkwAbja3b/Io56tgSuAvkA7YAHwJHCFu38cOCevts2sKXAqcC7QCWgNfAz8C7jR3d/KOr4y7k8fYNe4jc+AF4Eb3P31hDauAq7Mcanfc/cnc+xvlFauXMmXX35J06ZN2XTTTVmxYgVPP/10cCRBIr179+baa69l2LBhHHzwwQwenPxwfk0tW7aM3/72t4wYMYKvvvqKs846i6uuuoqK0BPgZaZNmzb8+Mc/5sc//jEzZszg7rvv5g9/+AMPPfQQZrZ2TcA5c+Zw5plnMmLECD755BO++CL6ddOsWTP22msvjj/++LVB3j777KMR6wQKUurfxhydFBGRwimKrJ5mthvwMrAd8DjwDtATOAx4F+jl7gvCNaytp21cT2fgOWAysCdwPDAfOMjdP8g6J++2zewh4GSiYO+vwFfA3kSB40qioOy5jONfBQ4ApgKvAUuASuBwYBVwirs/ltXGVUSB31hgdsLl/sHd/7Ohe9KYsnqOGzeOc845h+XLl68ta968Offcc48+sNTAmjVr6Nu3L08++ST/+Mc/OOigg2pVRzrD4kcffcTRRx/Nddddl5hltbFZvnw5O+2009oRvEzNmzfnzDPPXBvk7bXXXrRo0aIAvSxNWnqgNCirZ+FttVV3P/TQ9T8ThDIQ5vpbXVVVcnnfvsnlo0cnl+fKVDl4UnKmylGDkjNxLq7If23e0HK+oYyNoXuVSygj5YAByeWhDKGhbJ8A48cnl4f+jjukx8TE8qrRyRleIZzdMlQeev/kyoYZuo5QXaEsoKH3IYRfw9C9Ct333uFbFbzG0Hs3mL0z9OYh/PMxenTxZ/W8nSjwusDdf5suNLObgZ8Cw4lG1zZkBFHQd7O7/yyjnguAW+N2jqxL22bWgyjoewvo6e5LM/b9ELgHuJwo8EwbB5yWHaiZWX/gD8AYM/t/7v5NwjXd5+4v1ODaG71hw4atE/QBfPPNN8pEV0NNmjRh7NixdO/enZNOOonXX3+d7bbbrsbnP/PMM/z85z9n+vTp7L///owdO5bDDjtsI/a4tLRo0WK97JVpK1eu5I477mjgHpWPUplCKSIiUkgFT28Yj7gdTjSqlf33oCuBr4HTzSzngyxmtjlwenz8VVm7RwFzgCPMbNc6tp0+/9nMoC/2eLzdNrPQ3X+bNDrn7uOA94imfu6d4/KkBkIZ55SJrua22morHn30URYsWMD3v//9nNkP09544w2OOOIIDj/8cL788kv++Mc/MmnSJAV9CULJMZQ0Q0RERDa2ggd+RFMqAZ529zWZO9z9K6Ln5loBB26gngOBlsC/4vMy61kDPJXVXm3bTj+/9x0zy37g5ph4mzx+nmxlvA19wv6WmV1iZpeZ2Slmtk0edTcq+lBdPyorK/nd737Hc889xxVXXBE87qOPPuLMM88klUoxefJkbr75Zt555x2+//3va8mMgOHDh6+3xp+eRxMREZGGUAxTPdMLh80K7H+PaFSuM/BsHeshrqfWbbv7m2Z2C9E00HfM7P8RPePXjWga6YNEUz03yMwOBLoC1cCbgcOuzfp+hZndQJSspvAPaBYRJXmoP2eeeSYvv/wyI0eOZPny5Tz22GNrn58aNmwY77//PrfeeivuzqWXXsovfvGLnIuBS0RJM0RERKRQiiHwS68qvCiwP12+5Uaop1Ztu/vFZvYucAvwk4xdU4Gx7v71Bvqazj6afqL5p+6+OuuQN4CzgBeIsoxuRxSE/ooosGwKDN1QO42JPlTXr9tuu41nnnmGW265ZW3ZnDlzGDhwIACnn3461157LR06dChUF0uSnkcTERGRQiiGwK+kWLTA2a1EAd/lRMlZviTK0nkL8HczG+zugfxVED8z+DjRUhC/dveHs49x9+y8RnOBu8zsdeBV4BIzu9nd10sRaGYDgYHQ+KY56kN1/WnRogUrV65M3NeuXTvuvz+ciU1ERIpPx47JSQLbtFm/DMLZJSGcTXFIRfL/DdXVyRkIg2kcgUmBc0YGsncO6RvImBhKOwlUz0xOv2htktebbd8+uZ5c92rChPC+JKGMlCNGhM8JZU0N3pOhySf07BlOVZlv5tChtRieCN3HUBuhl3b8iMB1AzNJfv+EsqzWJutt6F6NGp1n9s5Qp4CKEYGfqRyK4UGc9Kha4NfO2vIvN0I9tTlnAHA+cJu7X+fuH7v7Enf/J3AssAy4Lk42s5446Psb8C2i7KOXBdpOFK/5NwnYBEjMt+/uY9y9u7t333bbbZMOEamReYHfwJ9++mkD90RERERE6qIYAr93423nwP5O8Tb0HF5d6qnNOekELs9nH+zunxKtA7g5/3t+cC0z2wL4O/BtopG+n2UfU0OfxducmU5F6koJc0RERETKQzEEfukA6nAzW6c/caDUC1hKNL0xl1eJRtt6xedl1tOE6Pm4zPZq2/am8TY0lJYuX2dNPjNrAzwN/B8wPN+Rvox6NgH2i7/9INexInWlLJQiIiIi5aHggZ+7v08UEHUEBmXtvppoVOuBzIQpZranme2ZVc8S4IH4+Kuy6hkc1/+Uu3+QcU7ebQMvxduL42BuLTM7F9gR+BR4O6N8K6IlHg4ErnT3nFk/zWwLM0saMWwO/AbYmWhkcUquekTqqn///owZM4YOHTpgZnTo0IExY8boOUoRERGRElMsyV1+ArwM3GZm3wVmAgcQrbM3CxiWdXz6iU3LKh8KHEoUlFUSPQvXBTgemM/6wV1t2r4d6A/sA8wys78QPQO4H/AdYDUwKCtL52NAd+B9oImZXZXQjwnuPj3+d1tgpplNifvzCdFI4mHALsDnwPez1x4U2RiUMEdERESk9BVF4Ofu75tZd+AaorXwjiIKdm4Frnb3L2pYzwIzOwi4EuhLNK1yAXAv0bp3H9e1bXdfYma9gIuBE4AfAM2Jnrt7GLjR3bPz/+wSb3eL+5ZkNjA9/vdCYBTQEzgC2Jpo6uj7wPVESWHm574bIiIiIiIiEdMa4OWte/fuPmWKZoSKiEhhmdlUd+9e6H40Zt332sunPLzeClIsDiyP0Duc2T+YRn9mIIt+6wFVieVVhJdzCAktjxBaaiGXiROTy0PXHlpWIJTyH8L9DS3PEFrOIdeyEMFlDQK3N7R6QJ8+4Tby9fbbyeVdKhYHz6kakLyMRuj6cqzUERRaMsL7Jr9HqUou7zkqvJzCpLHJPwgjJyT/rIWuI9eSEUOGZk98jBgEf9cW/Bk/ERERERER2bgU+ImIiIiIiJQ5BX4iIiIiIiJlToGfiIiIiIhImVPgJyIiIiIiUuaU1bPMmdlnwJxC96MAtiFa71Dqj+5p/dL9rH+6p/WvPu9pB3fftp7qklrI+kzQmH9eGuu1N9brhsZ17cHftQr8pCyZ2RSlDa9fuqf1S/ez/ume1j/d0/LVmF/bxnrtjfW6oXFfeyZN9RQRERERESlzCvxERERERETKnAI/KVdjCt2BMqR7Wr90P+uf7mn90z0tX435tW2s195Yrxsa97WvpWf8REREREREypxG/ERERERERMqcAj8REREREZEyp8BPSp6ZtTWzc8xsvJn9x8yWmdkiM/unmZ1tZnqf1wMzO83MPP46p9D9KVVm9t34vfqpma0ws3lm9pSZHVXovpUiMzvazJ42s4/jn/0PzOxhMzuo0H0rVmbWz8x+a2Yvmdni+Gf6Dxs452Aze8LMFsb3eYaZXWRmTRuq31J3Zrajmd0T/95ZYWazzew3ZrZVoftWV431fV3bz0DlcO0AZna9mT1rZh/F17HQzKaZ2ZVm1jZwTllce23oGT8peWZ2LvA74BPgeWAusD1wAtAGeBQ4yfVmrzUz2wn4N9AU2Bz4kbvfVdhelR4z+zVwKfAx8HeixWS3BfYHJrr7zwvYvZJjZtcDPwcWABOI7ufuwHFAM+AMd8/5wa8xMrPpwL7AEqL34p7AOHc/LXD88US/R5cDDwELgWOBPYBH3P2kBui21JGZ7Qa8DGwHPA68A/QEDgPeBXq5+4LC9bBuGuv7ujafgcrl2gHM7BvgdeBtYD6wGXAg0B2YBxzo7h9lHF82114r7q4vfZX0F/Adoh/aJlnlOxD9AnTgxEL3s1S/AAMmAu8DN8T385xC96vUvoAfxffuPqB5wv5NCt3HUvqKf75XA58C22XtOyy+1x8Uup/F+BXfn07xz/ah8b36Q+DY1kQfplYA3TPKWxAFEQ6cWuhr0leNXven4tfr/Kzym+Py3xe6j3W8vkb5vs73M1A5XXu634Hy4fG13F6u116bL02Bk5Ln7s+5+1/dfU1W+afA7+NvD23wjpWPC4j+Y/kh8HWB+1KSzGxTov+E5gID3f2b7GPcfWWDd6y0dSB6XOE1d5+fucPdnwe+IhpNlSzu/ry7v+fxJ54N6Ed0Hx909ykZdSwHLo+/PW8jdFPqUTzadzgwGxidtftKot/tp5vZZg3ctXrTWN/XtfgMVDbXDmv7neTP8bZTRllZXXttKPCTcpf+ML2qoL0oUWbWBbgOuNXdXyx0f0pYH6L/bB4D1sTPpV1mZhfqWbRaew/4BuhpZttk7jCzQ4AtiEaqpW6+E2+fTNj3IrAUODj+44YUr8Pi7dMJAcJXwL+AVkRT5BqDxvK+TvoM1Fiu/dh4OyOjrLFce1CzQndAZGMxs2bAGfG3ST/kkkN8/x4gGqUaWuDulLoe8XY5MA3YK3Onmb0I9HP3zxq6Y6XK3Rea2WVE09TeNrMJRM/67Ub0jN8zwI8L18OysUe8nZW9w91XmdmHQDdgV2BmQ3ZM8hJ8HWPvEY0IdgaebZAeFVbZv69zfAYqy2s3s0uIchC0IXq+71tEQd91GYeV5bXnQ4GflLPriD5gP+HuTxW6MyXoCiAFfMvdlxW6MyVuu3h7KdED6P8HTAd2AW4k+sD1MJqSnBd3/42ZzQbuIXqGMu0/wH3ZU0ClVtrE20WB/enyLTd+V6QO9DquqzHcj9BnoHK99kuIktqkPQmcmfUH1XK99hrTVE8pS2Z2AfAzoqxlpxe4OyXHzA4gGuW7yd1fKXR/ykD6d+0q4Dh3/6e7L3H3fwNVRBnovq1pn/kxs58DjxAlzNmNKJvb/sAHwLg4i6qISKPSGD8DufsO7m5ESW1OIBq1m2Zm+xW2Z8VFgZ+UHTMbDNxKNLJymLsvLHCXSko8PeR+oqkQvyxwd8rFl/F2mrvPztzh7kuJsu1BlFpdasDMDgWuB/7i7he7+wfuvtTdXycKpquBn5nZrgXsZjlI/wW8TWB/uvzLjd8VqQO9jusq2/tRg89AZXvtAO7+X3cfTzSTpi3R55m0sr72mlDgJ2XFzC4Cfgu8SfQL79PC9qgkbU70nEcXYLn9b9F2J8r+BnBnXPabQnWyxLwbb78M7P8i3rbc+F0pG8fE2+ezd8TB9CSi/+NSDdmpMpR+73bO3hH/kWgXopHsDxqyU5K34OsYS2c+DD0DWG7K8n1dw89AZXnt2dx9DlHw2y0jAVijuPZcFPhJ2YgTPdxC9OzUYXq+p9ZWAHcHvqbFx/wz/l7TQGvmWaL1gbqaWdLv3XSylw8brkslL511LbRkQ7p8vaUzJC/PxdsjE/YdQpQJ8mV3X9FwXZJaSP+B5PDs30FmtgXQiyij4asN3bECKbv3dR6fgcru2nNoH29Xx9vGdO2JFPhJWTCzXxI9yDwV+K67f17gLpUsd1/m7uckfQF/iQ8bG5c9VMi+lor4L49/BXYGLszcZ2aHA0cQjQYq+2zNvRRvB5pZReYOM/se0QfZ5USL8krtPQJ8DpxqZt3ThWbWAvhV/O3vCtExqTl3fx94GugIDMrafTXR87EPuHtjWau1rN7XeX4GKptrN7POZrbetE0za2Jmw4kSq73s7ulZNWVz7bVlNVvnUqR4mdkAouQOq4mmOCRla5rt7vc1YLfKkpldRTTd80fufleBu1NSzGxHoiBkJ6IRwGlE00r6Eo0GnurujxasgyUmHrV4CuhNtFj7eOBToinKxwAGXOTutxask0XKzPoSve8gSoRwBNHUpnQw/bm7X5J1/CNEgfSDwEKiJTP2iMtPruGi2VJA8SLuLxN9GH6cKF39AURr/M0CDnb3BYXrYd001vd1bT4DldG1XwSMJJqF9CHRkj7bA98mSu7yKVEg/HbGOX0pg2uvLQV+UvIygpFc/uHuh2783pQ3BX51Y2bbEi2TcRzQDlhM9KFkpLtPKmTfSpGZbUI0enEq0JVoms5Couf7bnP3pwvYvaJVg9+Zc9y9Y9Y5vYBhwEFAC6IlM+4hus+r16tBipKZ7QRcQzTVrS3wCdEfTa7OGBUpSY31fV3bz0Blcu17AecSrdm3I9EyDF8T/SHjb0TXsl6Cv3K49tpS4CciIiIiIlLm9IyfiIiIiIhImVPgJyIiIiIiUuYU+ImIiIiIiJQ5BX4iIiIiIiJlToGfiIiIiIhImVPgJyIiIiIiUuYU+ImIiIiIiJQ5BX4iInVkZoeamccL6YqIiIgUHQV+ItJg4uDIs8o6xuX3FahbG1QKfRQRERHJpVmhOyAiUgYmAV2AzwvdEREREZEkCvxEROrI3ZcC7xS6HyIiIiIhmuopIgUTPxP3YfztgPRU0PjrzKxjjzCzJ8zsczNbYWbvm9kNZrZlQr2z46/WZnZz/O+V6WfwzKy9mV1hZv8ys0/N7Bszm2dmfzSzrvn2MdczfmbWyczuN7PqjHbuN7NOSfcjrudQM+tnZpPMbKmZLTSzB82sIuGcXc1sjJn9x8yWxcf+28x+b2ZtN/ASiIiISCOhET8RKaQXgC2BC4E3gAkZ+6an/2FmVwJXAQuB/wfMB/YBLgGOMrOD3H1xVt3NgeeArYGngcX8L4A7BPgF8DzwKLAE6AT0A44zs17u/kY+fUxiZj2AicAWwF+At4E9gdOA482st7tPTjj1J8Bx8Tn/AA4ATgH2NbNKd18R198OmAy0Bp6Ir6UFsAtwOjAKWJCrjyIiItI4KPATkYJx9xfMbDZRUDXd3a/KPsbMDiMK+l4BjnL3LzP2nQncC1wN/DTr1HZEgda33f3rrH3PAdu7+1dZbe0L/Au4DvheTfuYxMwMuJ8oKDvN3cdl7DsFeBB4wMy6uvuarNOPBHq4+78zzvkj8H3geODPcXE/osD2Ine/Nav9zYDsekVERKSR0lRPESl2F8TbH2UGfQDufh/RqFv/wLk/Swj6cPf52UFfXP4GUVB4mJltUoc+AxxMNLr3SmbQF7fzEPBPYA/gWwnn3pYZ9MXujLc9E45fll3g7l+7+3rlIiIi0jhpxE9Eit1BwErgJDM7KWF/c2BbM2vr7pnTGpcDM0KVmtnRwLlAd2Ab1v99uA3wSR36vV+8fS6w/zmioC8FvJi1b0rC8R/F260yyv4CjABGm9kRwFNEI5Zvu7sjIiIiElPgJyLFri3R76orN3Dc5qz7PNv8UPBjZhcCvwG+AJ4B5gJLAQf6AvsCm9al00CbeBsKHtPlWybs+zKhbFW8bZoucPc5ZtaTaCrskcAJ8a6PzOxGd78tj/6KiIhIGVPgJyLFbhHQxN23zvO8UNDXjChQ+hTYz90/ydp/UG06mWBRvN0hsL9d1nG14u4zgVPi69oX6A2cD9xqZl+7+911qV9ERETKg57xE5FCWx1vmwb2vwpsZWbd6qm9bYhG2V5OCPo2539TNPPpY5Jp8fbQwP7D4u3redQZ5O6r3H2qu19PlAQGotFLEREREQV+IlJwXxCNzu0c2H9LvL3TzNpn7zSzzczswDzam080rXP/ONBL17MJcCtRYJhvH5P8C3gX+JaZ9cvqcz/g/4BZREleasXM9jezNgm7to+3S2tbt4iIiJQXTfUUkYJy9yVm9hrwf2Y2jigYWg38xd1nuPuzZvYLYCTwnpk9QbQe3+ZAB+DbRMHTkTVsb42Z3Ua0jt+/zexxogQxhxEtjfA8/xuNq1EfA+24mQ0geobwobidd4gyefYFvgLOSFjKIR+nAz82s38C7xMFqLsBxwIriJ5jFBEREVHgJyJF4XSikb0jiaYpGvAxcVZOd7/ezP5FtLTDt4jWslsEVANjgD/m2d4vgc+Ac4Afx3U9A1xOtCZg3n1M4u6vxYu4X0707N2xwOfAn4Br3f3dPPud7U9ESWgOBvYHWhLdkweBm9z9zTrWLyIiImXClPFbRERERESkvOkZPxERERERkTKnwE9ERERERKTMKfATEREREREpcwr8REREREREypwCPxERERERkTKnwE9ERERERKTMKfATEREREREpcwr8REREREREypwCPxERERERkTKnwE9ERERERKTM/X+IxHZw2zwHHwAAAABJRU5ErkJggg==\n", 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\n", 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Version Information

Qiskit SoftwareVersion
qiskit-terra0.22.0.dev0+4749eb5
qiskit-aer0.11.0
qiskit-nature0.5.0
qiskit-finance0.4.0
qiskit-optimization0.5.0
qiskit-machine-learning0.5.0
System information
Python version3.8.13
Python compilerClang 12.0.0
Python builddefault, Mar 28 2022 06:16:26
OSDarwin
CPUs2
Memory (Gb)12.0
Thu Sep 15 14:07:30 2022 EDT
" + "

Version Information

Qiskit SoftwareVersion
qiskit-terra0.22.0
qiskit-aer0.11.0
qiskit-ignis0.7.0
qiskit0.33.0
qiskit-machine-learning0.5.0
System information
Python version3.7.9
Python compilerMSC v.1916 64 bit (AMD64)
Python builddefault, Aug 31 2020 17:10:11
OSWindows
CPUs4
Memory (Gb)31.837730407714844
Thu Oct 13 10:46:23 2022 GMT Daylight Time
" ], "text/plain": [ "" @@ -423,46 +418,6 @@ "%qiskit_version_table\n", "%qiskit_copyright" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2116e41c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f32a9b27", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f6a4b09", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0e3658bc", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "92490ddc", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -481,7 +436,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.7.9" }, "rise": { "height": "90%",