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+file:: [MDE_Intelligence_2023_paper_18_1691442588326_0.pdf](../assets/MDE_Intelligence_2023_paper_18_1691442588326_0.pdf)
+file-path:: ../assets/MDE_Intelligence_2023_paper_18_1691442588326_0.pdf
+
+- quantum information theory and linear algebra
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+- application of AI for model transformations, concerning endeavours such as model translation [1], generation [2], or repair [3], [4]. 
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+- program features to classes while maintaining separation of concerns, in order to obtain a high-quality software model.
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+- Automated Software Engineering for Quantum Computing.
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+- conceptual model of quantum programs
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+- synthesis of quantum programs. The latter is particularly relevant for a broad adoption of QC, because designing a suitable quantum program for a desired computational task requires extensive knowledge in quantum information theory and linear algebra
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+- no MDO approaches for the automated synthesis of quantum programs
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+- holistic approach that supports the definition but also to facilitate development, in parts or as a whole, by leveraging AI methods
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+- Contributions
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+- ethod to conduct automated quantum program synthesis using the existing MDO engine MOMoT [7].
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+- Drawing from dedicated models for representing quantum programs and configurable MDO approaches, it would allow to further analyse the application of different AI and MDE approaches for quantum program synthesis in the future.
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+- roblem-agnostic environment for model optimization. 
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+- The Circuit Model
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+- arnessing quantum mechanical phenomen
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+- a quantum circuit comprises the application of quantum gates in an ordered manner 
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+- gates are sequentially applied to the quantum information, which is stored on so-called qubits.
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+- Lacking the information on the implementation of Oracles prohibits the execution of a quantum circuit [35].
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+- he required elementary quantum gates to realize a certain functionality, is known to be a highly non-trivial task which is required for the quantum program to be executable
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+- produced output quantum state and an expected target state
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+- Several approaches have been explored to automate the discovery and synthesis of quantum programs, where especially reinforcement learning [15]–[17], [36]–[38] and genetic programming approaches [39]–[41] have been studied.
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+- we will describe how the MOMoT framework can be applied to automatically synthesize quantum programs in terms of circuit models.
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+- a genetic algorithm is used to search for implementations that meet the trade-off between accuracy and computational cost present in the NISQ-era
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+- Model-Driven Optimization
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+- Quantum Circuit Model
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+- This set represents the found trade-off solutions of the multi-objective search and the Quantum Circuit Models are transformed back to the specific Q-SDK representation.
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+- AngleParameters
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+- This paper demonstrates the feasibility of using existing model-driven search approaches for automated quantum program synthesis
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+- We do not provide a performance evaluation of our proposed approach, which would comprise, among others, hyperparameter tuning, comparison of different search algorithms, and effects of additional transformation rules.
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+- We leave the according study of performance and scalability of our proposed approach as future work
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