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Quantum Data Management in the NISQ Era: Extended Version
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Quantum computing has emerged as a promising tool for transforming the landscape of computing technology. Recent efforts have applied quantum techniques to classical database challenges, such as query optimization, data integration, index selection, and transaction management. In this paper, we shift focus to a critical yet underexplored area: data management for quantum computing. We are currently in the noisy intermediate-scale quantum (NISQ) era, where qubits, while promising, are fragile and still limited in scale. After differentiating quantum data from classical data, we outline current and future data management paradigms in the NISQ era and beyond. We address the data management challenges arising from the emerging demands of near-term quantum computing. Our goal is to chart a clear course for future quantum-oriented data management research, establishing it as a cornerstone for the advancement of quantum computing in the NISQ era.
Forward citations
Cited by 3 Pith papers
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InferQ: A Database-Oriented Benchmark for Quantum Circuits Simulation
InferQ generates 202,975 compositional quantum circuits as SQL workloads and shows RDBMS engines beat Qiskit Aer on peak memory for ~51% of them, with ML selectors predicting the best backend at up to 95-97% accuracy.
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Quantum Information-Theoretical Size Bounds for Conjunctive Queries with Functional Dependencies
Worst-case conjunctive query size bounds can be reformulated with quantum Rényi entropy, producing sound but generally non-tight upper bounds whose classical tight version is recovered only in the α→1 limit.
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Qymera: Simulating Quantum Circuits using RDBMS
Qymera translates quantum circuits into SQL over integer-encoded state tables, runs them in SQLite or DuckDB, and provides a circuit builder and benchmarking tools.
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