用数据库思维模拟量子电路，提升效率并支持智能选型。
InferQ: A Database-Oriented Benchmark for Quantum Circuits Simulation
- 将量子电路编译为可由数据库执行的SQL任务，支持大规模系统研究。
- 在50%以上电路中，数据库内存表现优于主流Qiskit模拟器。
- 提供20万+电路数据集与特征，助力机器学习优化模拟器选择。
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以连接和聚合为主的张量收缩SQL工作负载来执行模拟。然而现有成果多集中于高度结构化的少数电路，缺乏对查询优化、物理设计及跨多种电路的引擎级评估等系统性研究支持。本文提出InferQ，一个面向数据库的量子电路模拟基准。InferQ通过组合电路模板生成通用且可组合的电路，将每个模拟任务转化为RDBMS可用的SQL工作负载，并提取电路与查询的静态、图结构、SQL及动态特征用于工作负载表征。同时发布包含202,975个电路的大规模数据集，并提供在线可视化工具支持搜索、过滤与下载。在PostgreSQL、SQLite、DuckDB和Umbra等RDBMS引擎以及广泛使用的Qiskit Aer模拟器上的实验表明，对于超过50%的InferQ生成电路，RDBMS在峰值内存使用上优于Qiskit Aer。此外，利用InferQ特征，轻量级机器学习模型（线性与树基模型）可准确预测何时采用SQL执行更优，运行时预测准确率达95.6%，内存预测达97.4%，支持以数据为中心的模拟器选择，为基于SQL的量子电路模拟的系统优化奠定基础。
原文摘要 · Abstract (English)
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.
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