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Entanglement Theory Quantum Correlations
Open Quantum Systems Decoherence
Quantum Machine Learning
ell-Multiranks of Multipartite Quantum States via Tensor Flattening: A Mathematica Codebase
arXiv
Authors: Masoud Gharahi
Year
2025
Paper ID
15960
Status
Preprint
Abstract Read
~2 min
Abstract Words
72
Citations
N/A
Abstract
We present a Mathematica codebase for computing ell-multilinear ranks $ell$-multiranks of multiqudit quantum states using tensor-flattening techniques. By calculating the ranks of all bipartition-induced matricizations, the method provides an efficient criterion for detecting Genuine Multipartite Entangled (GME) states in systems with local dimension d. The code automatically generates all required tensor reshapes and outputs the full ell-multirank profile, offering a practical tool for characterizing entanglement in high-dimensional multiqudit systems.
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- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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- We present a Mathematica codebase for computing ell-multilinear ranks ell-multiranks of multiqudit quantum states using tensor-flattening techniques.
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