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aiqbench takeWritten by an AI model; may contain errors — check the source.Quantum Computing

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This research proposes using convex polytope approximations in high dimensions to efficiently detect quantum entanglement, addressing the inherent computational difficulty of determining state separability. The study suggests that a finite set of approximate witnesses could suffice for high-probability entanglement determination without requiring full tomography. This methodological advance offers potential practical benefits for scaling quantum verification protocols and error correction in complex systems.
Sources coveringarXiv, Quantum Zeitgeist
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Entanglement Detection by Approximate Entanglement Witnesses

arXiv2 min read

arXiv:2402.14755v2 Announce Type: replace Abstract: The problem of determining whether a given quantum state is separable is known to be computationally difficult. We develop an approach to this problem based on approximations of convex polytopes in high dimensions. By showing that a convex polytope constructed from a finite number of hyperplanes approximates the Euclidean ball arbitrarily well in high dimensions, we find evidence that a finite set of approximate entanglement witnesses is potentially sufficient to determine the entanglement of a state with high probability.

Abstract:The problem of determining whether a given quantum state is separable is known to be computationally difficult. We develop an approach to this problem based on approximations of convex polytopes in high dimensions. By showing that a convex polytope constructed from a finite number of hyperplanes approximates the Euclidean ball arbitrarily well in high dimensions, we find evidence that a finite set of approximate entanglement witnesses is potentially sufficient to determine the entanglement of a state with high probability.

arXiv:2402.14755 [quant-ph]

(or arXiv:2402.14755v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2402.14755 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 168 words

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