A central requirement in asymmetric quantum nonlocality protocols, such as quantum steering, is the precise reconstruction of state assemblages-statistical ensembles of quantum states correlated with remote classical signals. Existing steering works often rely on simplifying assumptions about detection efficiency and photon loss. Here we introduce a generalized loss model for assemblage tomography that uses conical optimization techniques combined with maximum-likelihood estimation. This approach allows us to accurately estimate assemblages without assuming uniform detection efficiency on the untrusted party's side. Using an evidence-based framework grounded in the Akaike information criterion, we demonstrate faithful reconstructions while balancing model complexity. We validate our results through numerical simulations and an experimental setup, showing robust performance in assemblage estimation when applied to experimentally relevant data.

Publication:

PHYSICAL REVIEW A

http://dx.doi.org/10.1103/k686-kvdy

Author:

Yuanlong Wang

Centre for Quantum Dynamics and Centre for Quantum Computation and Communication Technology (CQC2T),Griffith University, Yuggera Country, Brisbane 4111, Australia

State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China

School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China

These authors contributed equally to this work

Contact author: wangyuanlong@amss.ac.cn