[logseq-plugin-git:commit] 2025-06-05T08:36:10.944Z

pages/MDE_Intelligence_2023_paper_18.md

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+type:: [[REVIEWS]]
+tags:: 
+year:: 2023
+venue:: [[MODELS-MDE_Intelligence]] 
+full-title:: Model-Driven Optimization for Quantum Program Synthesis with MOMoT
+date-start:: [[07-08-2023]] - 23:09
+date-submitted::
+external-links::
+status:: [[DONE]]
+deadline-submission:: [[08-08-2023]] 
+file:: ![MDE_Intelligence_2023_paper_18.pdf](../assets/MDE_Intelligence_2023_paper_18_1691442588326_0.pdf)
+
+	- [[Highlights]]
+		- ((64d26f3a-a110-4ed8-b93b-15483d2d1601))
+			- Are you improving them?
+	- [[Comments]]
+		- The paper suggests the utilization of the MOMoT framework, an established model-driven optimization approach, to tackle the challenge of automated synthesis of quantum programs.
+		- The paper maintains a commendable level of articulation and organization. It effectively introduces all foundational concepts, ensuring that the comprehension of the proposed solution remains accessible. It's worth noting that, as acknowledged by the authors, the paper lacks a comparative analysis between the proposed methodology and existing techniques. Consequently, the extent and nature of the proposed technique's superiority over established approaches, such as those based on reinforcement learning or genetic programming, remain unclear.
+		- Despite such limitations, I found the paper interesting and consisting of several elements that might trigger interesting discussions during the event.
+	-
+-