The field of quantum computing is shifting towards a more nuanced understanding of the role of deep learning in characterizing quantum systems. Researchers are reevaluating the necessity of deep learning models in certain tasks, such as ground-state learning, and exploring alternative approaches that leverage traditional machine learning methods. In the realm of grammar generation, significant progress is being made in developing more effective methods for generating high-quality test cases and inferring grammars from limited examples. Noteworthy papers include:

• A study that systematically benchmarks deep learning models against traditional machine learning approaches for ground-state learning tasks, revealing that ML models often achieve comparable or superior performance.
• A proposal of a novel hybrid genetic algorithm, HyGenar, which optimizes grammar generation using large language models and achieves substantial improvements in syntactic and semantic correctness.