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‎doc/pub/week15/html/week15-bs.html‎

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@@ -431,10 +431,10 @@ <h2 id="plan-for-the-week-of-may-5-9" class="anchor">Plan for the week of May 5-
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<li> Quantum neural networks (QNNs)</li>
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<ul>
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<li> Training QNNs and Loss Landscapes</li>
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<li> Implementing QNNs with PennyLane
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<!-- o <a href="https://youtu.be" target="_self">Video of lecture at</a> -->
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<!-- o <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf" target="_self">Whiteboard notes</a> --></li>
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<li> Implementing QNNs with PennyLane</li>
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</ul>
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<li> <a href="https://youtu.be" target="_self">Video of lecture at</a></li>
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<li> Whiteboard notes at <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf" target="_self"><tt>https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf</tt></a></li>
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<p>Mapping to Quantum States: Input data is first encoded into quantum
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states. This process is akin to a quantum feature map, where classical
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information is represented in the quantum Hilbert space.
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See whiteboard notes for examples.
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See whiteboard notes for examples at <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf" target="_self"><tt>https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf</tt></a>
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‎doc/pub/week15/html/week15-reveal.html‎

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<p><li> Training QNNs and Loss Landscapes</li>
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<p><li> Implementing QNNs with PennyLane
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<!-- o <a href="https://youtu.be" target="_blank">Video of lecture at</a> -->
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<!-- o <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf" target="_blank">Whiteboard notes</a> --></li>
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<p><li> Implementing QNNs with PennyLane</li>
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</ul>
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<p>
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<p><li> <a href="https://youtu.be" target="_blank">Video of lecture at</a></li>
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<p><li> Whiteboard notes at <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf" target="_blank"><tt>https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf</tt></a></li>
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<p>Mapping to Quantum States: Input data is first encoded into quantum
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states. This process is akin to a quantum feature map, where classical
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information is represented in the quantum Hilbert space.
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See whiteboard notes for examples.
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See whiteboard notes for examples at <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf" target="_blank"><tt>https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf</tt></a>
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‎doc/pub/week15/html/week15-solarized.html‎

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<li> Quantum neural networks (QNNs)</li>
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<ul>
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<li> Training QNNs and Loss Landscapes</li>
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<li> Implementing QNNs with PennyLane
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<!-- o <a href="https://youtu.be" target="_blank">Video of lecture at</a> -->
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<!-- o <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf" target="_blank">Whiteboard notes</a> --></li>
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<li> Implementing QNNs with PennyLane</li>
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</ul>
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<li> <a href="https://youtu.be" target="_blank">Video of lecture at</a></li>
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<li> Whiteboard notes at <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf" target="_blank"><tt>https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf</tt></a></li>
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<p>Mapping to Quantum States: Input data is first encoded into quantum
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states. This process is akin to a quantum feature map, where classical
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information is represented in the quantum Hilbert space.
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See whiteboard notes for examples.
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See whiteboard notes for examples at <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf" target="_blank"><tt>https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf</tt></a>
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‎doc/pub/week15/html/week15.html‎

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<li> Quantum neural networks (QNNs)</li>
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<li> Training QNNs and Loss Landscapes</li>
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<!-- o <a href="https://youtu.be" target="_blank">Video of lecture at</a> -->
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<!-- o <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf" target="_blank">Whiteboard notes</a> --></li>
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<li> Implementing QNNs with PennyLane</li>
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<li> <a href="https://youtu.be" target="_blank">Video of lecture at</a></li>
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<li> Whiteboard notes at <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf" target="_blank"><tt>https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf</tt></a></li>
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<p>Mapping to Quantum States: Input data is first encoded into quantum
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states. This process is akin to a quantum feature map, where classical
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information is represented in the quantum Hilbert space.
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See whiteboard notes for examples.
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See whiteboard notes for examples at <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf" target="_blank"><tt>https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesMay7.pdf</tt></a>
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‎doc/pub/week15/ipynb/ipynb-week15-src.tar.gz‎

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