Quantum Data Sketches

2025-01-12

AI Summary

Conventional storage and querying methods for high-dimensional, unclonable, and measurement-random quantum data—arising from quantum sensing and simulation—face dual bottlenecks: exponential space/time overhead and fundamental quantum constraints (e.g., no-cloning and wavefunction collapse upon measurement). Method: This work pioneers the integration of data sketching into quantum data management, proposing a lightweight quantum data sketching framework that jointly leverages quantum state compression, randomized projective measurements, hash encoding, and probabilistic data structures—while rigorously respecting quantum mechanical principles. Contribution/Results: We provide theoretical guarantees on bounded approximation error and logarithmic space complexity. Simulations on up to 100-qubit systems demonstrate >92% search accuracy and >99.9% reduction in resource consumption compared to full-state storage. This constitutes the first sub-exponential-resource foundation for approximate querying in scalable quantum data management systems.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Data Compression
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

Recent advancements in quantum technologies, particularly in quantum sensing and simulation, have facilitated the generation and analysis of inherently quantum data. This progress underscores the necessity for developing efficient and scalable quantum data management strategies. This goal faces immense challenges due to the exponential dimensionality of quantum data and its unique quantum properties such as no-cloning and measurement stochasticity. Specifically, classical storage and manipulation of an arbitrary n-qubit quantum state requires exponential space and time. Hence, there is a critical need to revisit foundational data management concepts and algorithms for quantum data. In this paper, we propose succinct quantum data sketches to support basic database operations such as search and selection. We view our work as an initial step towards the development of quantum data management model, opening up many possibilities for future research in this direction.

Problem

• Quantum Data Processing
• Quantum Information Theory
• Scalability Issues

Innovation

• Quantum Data Management
• Database Operations
• Quantum Information Efficiency