Problem
Research questions and friction points this paper is trying to address.
quantum circuit simulation
database benchmark
RDBMS
SQL workload
circuit characterization
InferQ: A Database-Oriented Benchmark for Quantum Circuits Simulation
🤖 AI Summary
This work addresses the lack of a general, scalable benchmark for evaluating SQL-based quantum circuit simulation methods within relational database management systems (RDBMS). To bridge this gap, we propose InferQ—the first large-scale quantum circuit benchmark tailored for RDBMS—leveraging compositional circuit templates to generate diverse quantum circuits, which are then compiled into SQL workloads. We characterize these workloads through multidimensional features, including static, graph-based, SQL-specific, and dynamic properties. Building on this benchmark, we employ lightweight machine learning models (linear and tree-based) to enable data-driven simulator selection and performance prediction. Experiments across 202,975 circuits demonstrate that mainstream RDBMS outperform Qiskit Aer in memory efficiency on over 50% of circuits, and our models achieve prediction accuracies of 95.3% for runtime and 97.4% for memory usage in identifying scenarios where SQL-based simulation is advantageous.
📝 Abstract
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 95.3% 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
quantum circuit simulation
database-oriented benchmark
SQL-based tensor contraction
workload characterization
machine learning for simulator selection
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