update week 14

Author: mhjensen

doc/pub/week14/html/week14-bs.html

@@ -183,6 +183,7 @@ <h2 id="plans-for-the-week-of-april-28-may-2" class="anchor">Plans for the week
 <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
 <ol>
 <li> Classical Support Vector Machines, reminder from last week</li>
+<li> Classical Kernels and transition to Quantum Kernels</li> 
 <li> Quantum Support Vector Machines</li>
 <li> And if we get time, we may start with Quantum Neural Networks
 <!-- o <a href="https://youtu.be/" target="_self">Video of lecture TBA</a> -->
@@ -197,7 +198,9 @@ <h2 id="plans-for-the-week-of-april-28-may-2" class="anchor">Plans for the week
 <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
 <ol>
 <li> For classical Support Vector Machines, see lecture notes by Morten Hjorth-Jensen at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter5.html" target="_self"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter5.html</tt></a></li>
-<li> Claudio Conti, Quantum Machine Learning (Springer), sections 1.5-1.12, see <a href="https://link.springer.com/book/10.1007/978-3-031-44226-1" target="_self"><tt>https://link.springer.com/book/10.1007/978-3-031-44226-1</tt></a>. With an IP-number from the university of Oslo, you can download this book for free. The material on quantum support vector machines will be discussed next week.</li>
+<li> Claudio Conti, Quantum Machine Learning (Springer), sections 1.5-1.12 and chapter 2, see <a href="https://link.springer.com/book/10.1007/978-3-031-44226-1" target="_self"><tt>https://link.springer.com/book/10.1007/978-3-031-44226-1</tt></a>.</li>
+<li> See also Maria Schuld's article at <a href="https://arxiv.org/abs/2101.11020" target="_self"><tt>https://arxiv.org/abs/2101.11020</tt></a>.</li>
+<li> For coding, we included examples using PennyLane at <a href="https://pennylane.ai/" target="_self"><tt>https://pennylane.ai/</tt></a> and Qiskit, see <a href="https://qiskit-community.github.io/qiskit-machine-learning/" target="_self"><tt>https://qiskit-community.github.io/qiskit-machine-learning/</tt></a></li>
 </ol>
 </div>
 </div>

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

@@ -199,6 +199,7 @@ <h2 id="plans-for-the-week-of-april-28-may-2">Plans for the week of April 28-May
 <p>
 <ol>
 <p><li> Classical Support Vector Machines, reminder from last week</li>
+<p><li> Classical Kernels and transition to Quantum Kernels</li> 
 <p><li> Quantum Support Vector Machines</li>
 <p><li> And if we get time, we may start with Quantum Neural Networks
 <!-- o <a href="https://youtu.be/" target="_blank">Video of lecture TBA</a> -->
@@ -212,7 +213,9 @@ <h2 id="plans-for-the-week-of-april-28-may-2">Plans for the week of April 28-May
 <p>
 <ol>
 <p><li> For classical Support Vector Machines, see lecture notes by Morten Hjorth-Jensen at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter5.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter5.html</tt></a></li>
-<p><li> Claudio Conti, Quantum Machine Learning (Springer), sections 1.5-1.12, see <a href="https://link.springer.com/book/10.1007/978-3-031-44226-1" target="_blank"><tt>https://link.springer.com/book/10.1007/978-3-031-44226-1</tt></a>. With an IP-number from the university of Oslo, you can download this book for free. The material on quantum support vector machines will be discussed next week.</li>
+<p><li> Claudio Conti, Quantum Machine Learning (Springer), sections 1.5-1.12 and chapter 2, see <a href="https://link.springer.com/book/10.1007/978-3-031-44226-1" target="_blank"><tt>https://link.springer.com/book/10.1007/978-3-031-44226-1</tt></a>.</li>
+<p><li> See also Maria Schuld's article at <a href="https://arxiv.org/abs/2101.11020" target="_blank"><tt>https://arxiv.org/abs/2101.11020</tt></a>.</li>
+<p><li> For coding, we included examples using PennyLane at <a href="https://pennylane.ai/" target="_blank"><tt>https://pennylane.ai/</tt></a> and Qiskit, see <a href="https://qiskit-community.github.io/qiskit-machine-learning/" target="_blank"><tt>https://qiskit-community.github.io/qiskit-machine-learning/</tt></a></li>
 </ol>
 </div>
 </section>

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

@@ -162,6 +162,7 @@ <h2 id="plans-for-the-week-of-april-28-may-2">Plans for the week of April 28-May
 <p>
 <ol>
 <li> Classical Support Vector Machines, reminder from last week</li>
+<li> Classical Kernels and transition to Quantum Kernels</li> 
 <li> Quantum Support Vector Machines</li>
 <li> And if we get time, we may start with Quantum Neural Networks
 <!-- o <a href="https://youtu.be/" target="_blank">Video of lecture TBA</a> -->
@@ -175,7 +176,9 @@ <h2 id="plans-for-the-week-of-april-28-may-2">Plans for the week of April 28-May
 <p>
 <ol>
 <li> For classical Support Vector Machines, see lecture notes by Morten Hjorth-Jensen at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter5.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter5.html</tt></a></li>
-<li> Claudio Conti, Quantum Machine Learning (Springer), sections 1.5-1.12, see <a href="https://link.springer.com/book/10.1007/978-3-031-44226-1" target="_blank"><tt>https://link.springer.com/book/10.1007/978-3-031-44226-1</tt></a>. With an IP-number from the university of Oslo, you can download this book for free. The material on quantum support vector machines will be discussed next week.</li>
+<li> Claudio Conti, Quantum Machine Learning (Springer), sections 1.5-1.12 and chapter 2, see <a href="https://link.springer.com/book/10.1007/978-3-031-44226-1" target="_blank"><tt>https://link.springer.com/book/10.1007/978-3-031-44226-1</tt></a>.</li>
+<li> See also Maria Schuld's article at <a href="https://arxiv.org/abs/2101.11020" target="_blank"><tt>https://arxiv.org/abs/2101.11020</tt></a>.</li>
+<li> For coding, we included examples using PennyLane at <a href="https://pennylane.ai/" target="_blank"><tt>https://pennylane.ai/</tt></a> and Qiskit, see <a href="https://qiskit-community.github.io/qiskit-machine-learning/" target="_blank"><tt>https://qiskit-community.github.io/qiskit-machine-learning/</tt></a></li>
 </ol>
 </div>
 

doc/pub/week14/html/week14.html

@@ -239,6 +239,7 @@ <h2 id="plans-for-the-week-of-april-28-may-2">Plans for the week of April 28-May
 <p>
 <ol>
 <li> Classical Support Vector Machines, reminder from last week</li>
+<li> Classical Kernels and transition to Quantum Kernels</li> 
 <li> Quantum Support Vector Machines</li>
 <li> And if we get time, we may start with Quantum Neural Networks
 <!-- o <a href="https://youtu.be/" target="_blank">Video of lecture TBA</a> -->
@@ -252,7 +253,9 @@ <h2 id="plans-for-the-week-of-april-28-may-2">Plans for the week of April 28-May
 <p>
 <ol>
 <li> For classical Support Vector Machines, see lecture notes by Morten Hjorth-Jensen at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter5.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter5.html</tt></a></li>
-<li> Claudio Conti, Quantum Machine Learning (Springer), sections 1.5-1.12, see <a href="https://link.springer.com/book/10.1007/978-3-031-44226-1" target="_blank"><tt>https://link.springer.com/book/10.1007/978-3-031-44226-1</tt></a>. With an IP-number from the university of Oslo, you can download this book for free. The material on quantum support vector machines will be discussed next week.</li>
+<li> Claudio Conti, Quantum Machine Learning (Springer), sections 1.5-1.12 and chapter 2, see <a href="https://link.springer.com/book/10.1007/978-3-031-44226-1" target="_blank"><tt>https://link.springer.com/book/10.1007/978-3-031-44226-1</tt></a>.</li>
+<li> See also Maria Schuld's article at <a href="https://arxiv.org/abs/2101.11020" target="_blank"><tt>https://arxiv.org/abs/2101.11020</tt></a>.</li>
+<li> For coding, we included examples using PennyLane at <a href="https://pennylane.ai/" target="_blank"><tt>https://pennylane.ai/</tt></a> and Qiskit, see <a href="https://qiskit-community.github.io/qiskit-machine-learning/" target="_blank"><tt>https://qiskit-community.github.io/qiskit-machine-learning/</tt></a></li>
 </ol>
 </div>