A quantum-to-classical-to-quantum protocol prepares states from classical shadows and measures observables directly, achieving exponential space savings for Clifford shadows and faster post-processing.
Quantum Data Sketches
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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.
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Improved Classical Shadow Tomography Using Quantum Computation
A quantum-to-classical-to-quantum protocol prepares states from classical shadows and measures observables directly, achieving exponential space savings for Clifford shadows and faster post-processing.