Bound entanglement in symmetric random induced states
Jonathan Louvet, François Damanet, Thierry Bastin
TL;DR
This work shows that symmetric random induced states (RIS) offer a practical route to generate PPT bound entangled states in multipartite systems for $N>3$, by tracing out ancillas from random pure states. It introduces two RIS-construction methods, MI and MII, and demonstrates that the probability of obtaining PPT BE can approach unity as system size grows, with distinct entanglement patterns across bipartitions. A Hilbert–Schmidt distance analysis reveals that MI yields a broader diversity of PPT BE states, while MII produces ensembles concentrated near the maximally mixed state and with wider parameter tolerance, making it more robust experimentally. Overall, symmetric RIS provide a versatile toolkit for generating large families of PPT BE states without heavy numerical optimization, with implications for entanglement theory, metrology, and quantum communication.
Abstract
Bound entanglement, a weak -- yet resourceful -- form of quantum entanglement, remains notoriously hard to detect and construct. We address this in this paper by leveraging symmetric random induced states, where positive partial transpose (PPT) bound entanglement arises naturally under partial tracing when proper parameters are selected. We investigate the probability of finding PPT bound entanglement in symmetric random induced states constructed via two methods: partial tracing of symmetric multiqubit pure states on the one hand (MI) and tracing out a qudit ancilla on the other hand (MII). For $N > 3$ qubits, we demonstrate that bound entanglement naturally emerges under optimal parameters, with a probability of occurrence very close to 1. We show that the two methods produce different varieties of PPT bound entangled states, and identify the contexts in which each method offers distinct advantages. These methods provide a versatile toolkit for the generation of large families of random PPT bound entangled states without complex numerical optimization.
