# Quantum Data Management in the NISQ Era: Extended Version

arXiv: https://arxiv.org/abs/2409.14111 · HTML: https://mlsanity.com/paper/2409.14111/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2024.

## Built on by (most cited first, 3 of 3)

- [Quantum delegated and federated learning via quantum homomorphic encryption](https://mlsanity.com/paper/2409.19359/) (2024)
- [Qute: Towards Quantum-Native Database](https://mlsanity.com/paper/2602.14699/) (2026)
- [InferQ: A Database-Oriented Benchmark for Quantum Circuits Simulation](https://mlsanity.com/paper/2607.29134/) (2026)

## Abstract (arXiv)

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.