Computable and Faithful Lower Bound for Entanglement Cost

• URL: http://arxiv.org/abs/2311.10649v1
• Date: Fri, 17 Nov 2023 17:07:26 GMT
• Title: Computable and Faithful Lower Bound for Entanglement Cost
• Authors: Xin Wang, Mingrui Jing, Chengkai Zhu
• Abstract summary: This paper proposes computable and faithful lower bounds for the entanglement cost of general quantum states and quantum channels. We also establish the best-known lower bound for the entanglement cost of arbitrary dimensional isotropic states.
• Score: 5.7169366634489895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum entanglement is a crucial resource in quantum information processing. However, quantifying the entanglement required to prepare quantum states and implement quantum processes remains challenging. This paper proposes computable and faithful lower bounds for the entanglement cost of general quantum states and quantum channels. We introduce the concept of logarithmic $k$-negativity, a generalization of logarithmic negativity, to establish a general lower bound for the entanglement cost of quantum states under quantum operations that completely preserve the positivity of partial transpose (PPT). This bound is efficiently computable via semidefinite programming and is non-zero for any entangled state that is not PPT, making it faithful in the entanglement theory with non-positive partial transpose. Furthermore, we delve into specific and general examples to demonstrate the advantages of our proposed bounds compared with previously known computable ones. Notably, we affirm the irreversibility of asymptotic entanglement manipulation under PPT operations for full-rank entangled states and the irreversibility of channel manipulation for amplitude damping channels. We also establish the best-known lower bound for the entanglement cost of arbitrary dimensional isotropic states. These findings push the boundaries of understanding the structure of entanglement and the fundamental limits of entanglement manipulation.

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