Update week15.do.txt

Author: mhjensen

doc/src/week15/week15.do.txt

@@ -1469,3 +1469,12 @@ measurement, and classical optimization.  As hardware improves, more
 complex QML tasks (e.g. image recognition, chemistry simulations,
 finance models) may become feasible.
 
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+#A. Abbas et al., “The power of quantum neural networks,” Nature Comput. Sci. 1, 403–409 (2021) .
+#E. Anschuetz and B. Kiani, “Quantum variational algorithms are swamped with traps,” Nat. Commun. 13, 7760 (2022) .
+#J. Zhang et al. (Wells Fargo Quantum Research), arXiv:2412.12484 (2024) – discusses QNN architectures and variationa#l circuits .
+#J. Millet et al., “Variational quantum circuits for machine learning: an application for detecting weak signals,” Ap#pl. Sci. 11, 6427 (2021) .
+#M. Zhao et al., “A tutorial on quantum machine learning and quantum neural networks,” arXiv:2504.16131 (2025) .
+#D. M. Houshmand, Quantum Machine Learning Lecture Notes, (online, 2023) . (Tutorial using PennyLane QNNs.)
+#PennyLane documentation and tutorials: 〈https://pennylane.ai〉 (for code examples and parameter-shift rules).