Density Matrix Diagonal-Block Lovas-Andai-type singular-value ratios for qubit-qudit separability/PPT probability analyses
Paul B. Slater

TL;DR
This paper explores the behavior of singular value ratios in higher-dimensional quantum states to extend separability probability analyses, building on Lovas and Andai's foundational work and employing novel numerical methods.
Contribution
It introduces a study of three singular value ratios in larger quantum systems, aiming to extend the Lovas-Andai separability function framework to higher dimensions.
Findings
Analyzed singular value ratios in 6x6 and 8x8 density matrices.
Initiated a numerical approach for higher-dimensional separability analysis.
Connected findings to Lovas's 2017 theoretical framework.
Abstract
An important variable in the 2017 analysis of Lovas and Andai, formally establishing the Hilbert-Schmidt separability probability conjectured by Slater of for the 9-dimensional convex set of two-rebit density matrices, was the ratio () of the two singular values () of . There, and were the diagonal blocks of a two-rebit density matrix . Working within the Lovas-Andai "separability function" () framework, Slater was able to verify further conjectures of Hilbert-Schmidt separability probabilities of and for the 15-dimensional and 26-dimensional convex sets of two-qubit and two-quater[nionic]-bit density matrices. Here, we investigate the behavior of the three…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Quantum Mechanics and Applications
