Aadi Patwardhan

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.

Quantum error correction (QEC) experiments generate large tabular datasets that researchers use to train AI-based decoders. However, QEC datasets are heterogeneous, scarce, and fragmented: schemas differ across code families and hardware platforms, datasets are scattered across incompatible repositories, hindering fair decoder comparison. We present QEC lake, a domain-specific data lake for quantum error correction. QEC lake continuously crawls and refreshes open QEC datasets, produces user-configurable simulated data, generates synthetic variants from real data, and manages data and metadata for discovery, query, and reuse. The system also offers AI-assisted dataset exploration and a model-oriented data preparation pipeline that converts heterogeneous QEC data into training-ready inputs for tree-based models and tabular foundation models. In the demo, attendees ingest sources, generate new datasets, explore them through structured and natural-language interfaces, and export model-ready artifacts.