发表机构
Delft University of Technology（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究人员提出面向数据库的量子电路模拟基准InferQ，生成大量通用电路数据集，实验发现超50%电路上RDBMS内存表现优于Qiskit Aer，结合其特征的轻量级模型可精准选择模拟器。
AI 中文摘要
近期研究表明，关系型数据库管理系统（RDBMS）可通过将量子电路模拟编译为SQL负载（主要是连接聚合张量收缩）来执行量子电路模拟。尽管早期结果颇具前景，但这些研究大多聚焦于一组高度结构化的窄范围电路，对数据库研究的系统性支持有限，例如在广泛电路范围内的查询优化、物理设计及引擎级评估。我们提出InferQ，一种面向数据库的量子电路模拟基准。InferQ通过从一组电路模板中组装子电路来生成通用、可组合的电路，将每个模拟任务输出为可用于RDBMS的SQL负载，并提取电路与查询特征（静态、图、SQL及动态特征）以进行负载表征。InferQ还在线发布了包含202975个电路的大型数据集，配套基于网页的查看器，支持电路与特征记录的搜索、过滤及下载。在针对RDBMS引擎（PostgreSQL、SQLite、DuckDB和Umbra）与广泛使用的Qiskit Aer模拟器开展的实验中，我们发现，在InferQ生成的超过50%的电路上，RDBMS的峰值内存使用量优于Qiskit Aer。此外，利用InferQ的特征，轻量级机器学习模型（线性模型与树模型）可准确预测何时采用SQL执行更具优势（运行时准确率达95.3%，内存准确率达97.4%），从而支持以数据为中心的模拟器选择，并为基于SQL的量子电路模拟的系统性优化打开大门。
英文摘要
Recent work suggests that relational database management systems (RDBMSs) can execute quantum circuit simulation by compiling the simulation into SQL workloads (primarily join-and-aggregate tensor contractions). While early results are promising, they largely focus on a narrow set of highly structured circuits and offer limited support for systematic database research, such as query optimization, physical design, and engine-level evaluation across a broad range of circuits. We present InferQ, a database-oriented benchmark for quantum circuit simulation. InferQ generates general, compositional circuits by assembling subcircuits from a set of circuit templates, emits each simulation task as an RDBMS-ready SQL workload, and extracts circuit and query features (static, graph, SQL, and dynamic) for workload characterization. InferQ also releases a large dataset of 202,975 circuits online, with a web-based viewer to support searching, filtering, and downloading circuits and feature records. In experiments across RDBMS engines (PostgreSQL, SQLite, DuckDB, and Umbra) and the widely used Qiskit Aer simulator, we find that RDBMSs achieve better peak memory usage than Qiskit Aer on more than 50% of the circuits generated by InferQ. Moreover, using InferQ features, lightweight machine learning models (linear and tree-based models) can accurately predict when SQL execution is preferable (with accuracy up to 95.6% for runtime and 97.4% for memory), enabling data-centric simulator selection and opening the door to principled optimization of SQL-based quantum circuit simulation.
CommentsAccepted for presentation at ACM SIGMOD 2027 and publication in the Proceedings of the ACM on Management of Data (PACMMOD). This arXiv version is an extended technical report that includes the complete appendix
DOI：10.1145/383
