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Open Quantum Systems Decoherence Quantum Machine Learning

Unconditionally secure quantum commitments with preprocessing

arXiv

Authors: Luowen Qian

Year

2023

Paper ID

6440

Status

Preprint

Abstract Read

~2 min

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Abstract

We demonstrate how to build computationally secure commitment schemes with the aid of quantum auxiliary inputs without unproven complexity assumptions. Furthermore, the quantum auxiliary input can be either sampled in uniform exponential time or prepared in at most doubly exponential time, without relying on an external trusted third party. Classically, this remains impossible without first proving mathsf{P} neq mathsf{NP}.

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This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2023 reference point for readers tracking recent quantum research.
We demonstrate how to build computationally secure commitment schemes with the aid of quantum auxiliary inputs without unproven complexity assumptions.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2311.18171 [2] arXiv https://arxiv.org/abs/2311.18171 [3] Publisher https://arxiv.org/abs/2311.18171

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