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‎doc/pub/week14/html/week14-bs.html‎

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None,
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'pennylane-implementations'),
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('Iris Dataset', 2, None, 'iris-dataset'),
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('Qiskit implementation', 2, None, 'qiskit-implementation'),
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('Plans for next week', 2, None, 'plans-for-next-week')]}
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end of tocinfo -->
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@@ -308,6 +309,7 @@
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<!-- navigation toc: --> <li><a href="#discussion-of-implementation" style="font-size: 80%;">Discussion of Implementation</a></li>
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<!-- navigation toc: --> <li><a href="#pennylane-implementations" style="font-size: 80%;">PennyLane implementations</a></li>
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<!-- navigation toc: --> <li><a href="#iris-dataset" style="font-size: 80%;">Iris Dataset</a></li>
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<!-- navigation toc: --> <li><a href="#qiskit-implementation" style="font-size: 80%;">Qiskit implementation</a></li>
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<!-- navigation toc: --> <li><a href="#plans-for-next-week" style="font-size: 80%;">Plans for next week</a></li>
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</ul>
@@ -2284,6 +2286,72 @@ <h2 id="iris-dataset" class="anchor">Iris Dataset </h2>
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</div>
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<!-- !split -->
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<h2 id="qiskit-implementation" class="anchor">Qiskit implementation </h2>
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<p>A similar implementation on Qiskit is given here (you need to have installed Qiskit as well).</p>
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<!-- code=python (!bc pycod) typeset with pygments style "default" -->
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<div class="cell border-box-sizing code_cell rendered">
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<div class="input">
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<div class="inner_cell">
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<div class="input_area">
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<div class="highlight" style="background: #f8f8f8">
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<pre style="line-height: 125%;"><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">qiskit</span> <span style="color: #008000; font-weight: bold">import</span> BasicAer
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">qiskit.utils</span> <span style="color: #008000; font-weight: bold">import</span> QuantumInstance
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">qiskit.circuit.library</span> <span style="color: #008000; font-weight: bold">import</span> ZZFeatureMap
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">qiskit_machine_learning.algorithms</span> <span style="color: #008000; font-weight: bold">import</span> QSVC
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.datasets</span> <span style="color: #008000; font-weight: bold">import</span> load_iris
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> StandardScaler
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<span style="color: #408080; font-style: italic"># Load and preprocess dataset</span>
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dataset <span style="color: #666666">=</span> load_iris()
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X <span style="color: #666666">=</span> dataset<span style="color: #666666">.</span>data
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y <span style="color: #666666">=</span> dataset<span style="color: #666666">.</span>target
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<span style="color: #408080; font-style: italic"># For simplicity, we&#39;ll only classify between two classes</span>
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X <span style="color: #666666">=</span> X[y <span style="color: #666666">!=</span> <span style="color: #666666">2</span>]
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y <span style="color: #666666">=</span> y[y <span style="color: #666666">!=</span> <span style="color: #666666">2</span>]
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<span style="color: #408080; font-style: italic"># Standardize features</span>
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scaler <span style="color: #666666">=</span> StandardScaler()
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X <span style="color: #666666">=</span> scaler<span style="color: #666666">.</span>fit_transform(X)
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<span style="color: #408080; font-style: italic"># Split into training and test sets</span>
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X_train, X_test, y_train, y_test <span style="color: #666666">=</span> train_test_split(X, y, test_size<span style="color: #666666">=0.2</span>, random_state<span style="color: #666666">=42</span>)
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<span style="color: #408080; font-style: italic"># Define quantum feature map</span>
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feature_map <span style="color: #666666">=</span> ZZFeatureMap(feature_dimension<span style="color: #666666">=4</span>, reps<span style="color: #666666">=2</span>)
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<span style="color: #408080; font-style: italic"># Set up quantum instance</span>
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quantum_instance <span style="color: #666666">=</span> QuantumInstance(BasicAer<span style="color: #666666">.</span>get_backend(<span style="color: #BA2121">&#39;qasm_simulator&#39;</span>), shots<span style="color: #666666">=1024</span>)
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<span style="color: #408080; font-style: italic"># Initialize QSVC</span>
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qsvc <span style="color: #666666">=</span> QSVC(quantum_instance<span style="color: #666666">=</span>quantum_instance, feature_map<span style="color: #666666">=</span>feature_map)
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<span style="color: #408080; font-style: italic"># Train QSVC</span>
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qsvc<span style="color: #666666">.</span>fit(X_train, y_train)
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<span style="color: #408080; font-style: italic"># Evaluate QSVC</span>
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accuracy <span style="color: #666666">=</span> qsvc<span style="color: #666666">.</span>score(X_test, y_test)
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<span style="color: #008000">print</span>(<span style="color: #BA2121">f&#39;QSVC accuracy: </span><span style="color: #BB6688; font-weight: bold">{</span>accuracy <span style="color: #666666">*</span> <span style="color: #666666">100</span><span style="color: #BB6688; font-weight: bold">:</span><span style="color: #BA2121">.2f</span><span style="color: #BB6688; font-weight: bold">}</span><span style="color: #BA2121">%&#39;</span>)
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</pre>
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</div>
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</div>
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</div>
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</div>
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<div class="output_wrapper">
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<div class="output">
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<div class="output_subarea output_stream output_stdout output_text">
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</div>
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</div>
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</div>
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</div>
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</div>
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<!-- !split -->
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<h2 id="plans-for-next-week" class="anchor">Plans for next week </h2>
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<ol>

‎doc/pub/week14/html/week14-reveal.html‎

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</div>
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</section>
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<section>
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<h2 id="qiskit-implementation">Qiskit implementation </h2>
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<p>A similar implementation on Qiskit is given here (you need to have installed Qiskit as well).</p>
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<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
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<div class="cell border-box-sizing code_cell rendered">
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<div class="input">
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<div class="inner_cell">
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<div class="input_area">
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<div class="highlight" style="background: #eeeedd">
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<pre style="font-size: 80%; line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">qiskit</span> <span style="color: #8B008B; font-weight: bold">import</span> BasicAer
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">qiskit.utils</span> <span style="color: #8B008B; font-weight: bold">import</span> QuantumInstance
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">qiskit.circuit.library</span> <span style="color: #8B008B; font-weight: bold">import</span> ZZFeatureMap
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">qiskit_machine_learning.algorithms</span> <span style="color: #8B008B; font-weight: bold">import</span> QSVC
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> load_iris
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.preprocessing</span> <span style="color: #8B008B; font-weight: bold">import</span> StandardScaler
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<span style="color: #228B22"># Load and preprocess dataset</span>
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dataset = load_iris()
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X = dataset.data
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y = dataset.target
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<span style="color: #228B22"># For simplicity, we&#39;ll only classify between two classes</span>
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X = X[y != <span style="color: #B452CD">2</span>]
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y = y[y != <span style="color: #B452CD">2</span>]
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<span style="color: #228B22"># Standardize features</span>
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scaler = StandardScaler()
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X = scaler.fit_transform(X)
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<span style="color: #228B22"># Split into training and test sets</span>
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=<span style="color: #B452CD">0.2</span>, random_state=<span style="color: #B452CD">42</span>)
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<span style="color: #228B22"># Define quantum feature map</span>
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feature_map = ZZFeatureMap(feature_dimension=<span style="color: #B452CD">4</span>, reps=<span style="color: #B452CD">2</span>)
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<span style="color: #228B22"># Set up quantum instance</span>
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quantum_instance = QuantumInstance(BasicAer.get_backend(<span style="color: #CD5555">&#39;qasm_simulator&#39;</span>), shots=<span style="color: #B452CD">1024</span>)
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<span style="color: #228B22"># Initialize QSVC</span>
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qsvc = QSVC(quantum_instance=quantum_instance, feature_map=feature_map)
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<span style="color: #228B22"># Train QSVC</span>
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qsvc.fit(X_train, y_train)
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<span style="color: #228B22"># Evaluate QSVC</span>
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accuracy = qsvc.score(X_test, y_test)
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<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&#39;QSVC accuracy: {</span>accuracy * <span style="color: #B452CD">100</span><span style="color: #CD5555">:.2f}%&#39;</span>)
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</pre>
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</div>
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</div>
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</div>
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</div>
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<div class="output_wrapper">
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<div class="output">
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<div class="output_subarea output_stream output_stdout output_text">
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</div>
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</div>
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</div>
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</div>
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</div>
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</section>
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<section>
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<h2 id="plans-for-next-week">Plans for next week </h2>
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<ol>

‎doc/pub/week14/html/week14-solarized.html‎

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None,
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'pennylane-implementations'),
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('Iris Dataset', 2, None, 'iris-dataset'),
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('Qiskit implementation', 2, None, 'qiskit-implementation'),
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('Plans for next week', 2, None, 'plans-for-next-week')]}
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end of tocinfo -->
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</div>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="qiskit-implementation">Qiskit implementation </h2>
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<p>A similar implementation on Qiskit is given here (you need to have installed Qiskit as well).</p>
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<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
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<div class="cell border-box-sizing code_cell rendered">
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<div class="input">
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<div class="inner_cell">
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<div class="input_area">
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<div class="highlight" style="background: #eeeedd">
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<pre style="line-height: 125%;"><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">qiskit</span> <span style="color: #8B008B; font-weight: bold">import</span> BasicAer
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">qiskit.utils</span> <span style="color: #8B008B; font-weight: bold">import</span> QuantumInstance
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">qiskit.circuit.library</span> <span style="color: #8B008B; font-weight: bold">import</span> ZZFeatureMap
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">qiskit_machine_learning.algorithms</span> <span style="color: #8B008B; font-weight: bold">import</span> QSVC
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> load_iris
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.preprocessing</span> <span style="color: #8B008B; font-weight: bold">import</span> StandardScaler
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<span style="color: #228B22"># Load and preprocess dataset</span>
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dataset = load_iris()
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X = dataset.data
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y = dataset.target
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<span style="color: #228B22"># For simplicity, we&#39;ll only classify between two classes</span>
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X = X[y != <span style="color: #B452CD">2</span>]
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y = y[y != <span style="color: #B452CD">2</span>]
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<span style="color: #228B22"># Standardize features</span>
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scaler = StandardScaler()
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X = scaler.fit_transform(X)
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<span style="color: #228B22"># Split into training and test sets</span>
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=<span style="color: #B452CD">0.2</span>, random_state=<span style="color: #B452CD">42</span>)
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<span style="color: #228B22"># Define quantum feature map</span>
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feature_map = ZZFeatureMap(feature_dimension=<span style="color: #B452CD">4</span>, reps=<span style="color: #B452CD">2</span>)
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<span style="color: #228B22"># Set up quantum instance</span>
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quantum_instance = QuantumInstance(BasicAer.get_backend(<span style="color: #CD5555">&#39;qasm_simulator&#39;</span>), shots=<span style="color: #B452CD">1024</span>)
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<span style="color: #228B22"># Initialize QSVC</span>
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qsvc = QSVC(quantum_instance=quantum_instance, feature_map=feature_map)
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<span style="color: #228B22"># Train QSVC</span>
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qsvc.fit(X_train, y_train)
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<span style="color: #228B22"># Evaluate QSVC</span>
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accuracy = qsvc.score(X_test, y_test)
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<span style="color: #658b00">print</span>(<span style="color: #CD5555">f&#39;QSVC accuracy: {</span>accuracy * <span style="color: #B452CD">100</span><span style="color: #CD5555">:.2f}%&#39;</span>)
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</pre>
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</div>
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</div>
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<div class="output_wrapper">
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<div class="output">
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<div class="output_area">
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<div class="output_subarea output_stream output_stdout output_text">
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</div>
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</div>
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</div>
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</div>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="plans-for-next-week">Plans for next week </h2>
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<ol>

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