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Characterizing local state distinguishability with non-maximal entanglement

Atanu Bhunia1,2,3,*, Priyabrata Char3,4,†, Subrata Bera3,‡, Indranil Biswas3,§, Indrani Chattopadhyay3,∥, and Debasis Sarkar3,¶

  • *Contact author: atanu.bhunia31@gmail.com
  • †Contact author: priyabrata_ipdf@iitg.ac.in
  • ‡Contact author: 98subratabera@gmail.com
  • §Contact author: indranilbiswas74@gmail.com
  • ∥Contact author: icappmath@caluniv.ac.in
  • Contact author: dsarkar1x@gmail.com, dsappmath@caluniv.ac.in
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APS Open Sci. 1, 000147 – Published 22 September, 2026

DOI: https://doi.org/10.1103/h7kx-vmtx

Abstract

Perfect LOCC (local operations and classical communication) discrimination of locally indistinguishable quantum states usually requires maximally entangled states as a resource. Whether nonmaximally entangled resources can enable perfect LOCC discrimination of locally indistinguishable states remains an interesting area of research. The states in the three-qubit Greenberger-Horne-Zeilinger (GHZ)-SLOCC (stochastic local operation and classical communication) class can display various entanglement distribution patterns, including cases with no bipartite entanglement in any reduced subsystems, or instances where bipartite entanglement occurs across one, two, or all three reduced subsystems. Significant research has explored how such states can be used in entanglement-assisted discrimination tasks. In this paper, we investigate the distinguishability of orthogonal product states using GHZ-SLOCC class entangled states, and we identify two nonmaximal GHZ-SLOCC states that facilitate perfect distinguishability. Additionally, we analyze the relationship between the probability of error and the amount of bipartite and multiparty entanglement. Our analysis focuses on how the distribution of entanglement affects the accuracy of state discrimination.

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APS Open Sci. 1, 000147– Published 22 September, 2026
Vol. 1
  • Received 22 June 2025
  • Accepted 11 September 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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