ABSTRACT As large language models (LLMs) continue to advance, they have demonstrated remarkable performance across a wide range of tasks. However, their application in highly specialized scientific domains—such as quantum physics—remains limited, particularly in educational settings where precision and domain expertise are crucial. Given the abstract nature and mathematical rigor of quantum theory, delivering accurate AI‐assisted support in this field is both technically demanding and educationally valuable. To address these challenges, we propose a retrieval‐augmented generation (RAG) framework that incorporates a domain‐specific knowledge base constructed from authoritative quantum physics textbooks. Our approach introduces customized strategies at both the preretrieval and postretrieval stages to enhance the relevance of retrieved content, mitigate hallucinations, and improve factual accuracy and contextual alignment. This tighter integration between retrieval and generation substantially improves the system's ability to produce reliable and pedagogically meaningful responses. To enable rigorous and systematic evaluation, we present PhysicBench—the first comprehensive benchmark specifically designed for quantum physics question answering in RAG systems. Unlike existing datasets, which are either scarce or ill‐suited to this domain, PhysicBench supports fine‐grained evaluation across key components of the RAG pipeline, including document retrieval, ranking effectiveness, and answer generation accuracy. It fills a critical gap in current resources and provides detailed metrics for performance analysis at each stage. Experimental results show that our proposed system significantly outperforms LLM‐only baselines, highlighting the effectiveness of domain‐aware RAG approaches. Overall, this work contributes both a robust technical solution and a much‐needed benchmark to advance research in AI‐powered education and intelligent question answering within the field of quantum physics.

Workload execution can account for 90% of the total database knob tuning time, which is often the bottleneck for efficient knob tuning in practice. Reducing the tuning time by using a compressed workload is a natural solution. However, many existing workload compression methods are designed for OLAP workloads, which reduce the number of queries needed for analysis tasks by sampling a small subset of queries. These methods are less effective for OLTP workloads in knob-tuning tasks, as they often disregard essential contextual details, including query sequence and concurrency. As a result, configurations that perform well on the compressed OLTP workload may not deliver similar competitive performance on the original workload. To address these challenges, we first define the objective of OLTP workload compression for knob tuning. We then propose a slice-based compression method, SCompression , which compresses workloads by slicing based on time intervals while preserving concurrency. SCompression achieves the objective by focusing on generating a compressed workload that (1) executes faster than the original workload and (2) produces performance variations similar to the source workload under different configurations. SCompression works in three steps: (1) dividing the workload into segments to capture regular performance fluctuations, (2) slicing each segment to preserve concurrency and transaction context, and (3) sampling slices under execution time constraints using a cluster-based approach to ensure representativeness. Finally, SCompression replays the compressed workload to produce the performance that mirrors the source workload. Extensive experiments on real-world and benchmark OLTP workloads show that SCompression is a cost-effective solution for knob tuning, accelerating tuning by up to 40× with only a 5% performance reduction.