Paper published in IEEE Transactions on Information Theory!

Paper on quantum information recovery is published in IEEE Transactions on Information Theory 🎉

Our paper Virtual Quantum Markov Chains by Yu-Ao Chen, Chengkai Zhu, Keming He, Mingrui Jing, and Xin Wang is published in IEEE Transactions on Information Theory!

For IEEE TIT: The IEEE Transactions on Information Theory publishes papers concerned with the transmission, processing, and utilization of information. While the boundaries of acceptable subject matter are intentionally not sharply delimited, its scope currently includes Shannon theory, coding theory and techniques, data compression, sequences, signal processing, detection and estimation, pattern recognition, learning and inference, communications and communication networks, complexity and cryptography, and quantum information theory and coding. Papers published in the IEEE Transactions on Information Theory should contain a strong conceptual or analytical contribution.

For the paper Virtual Quantum Markov Chains: Quantum Markov chains generalize classical Markov chains for random variables to the quantum realm and exhibit unique inherent properties, making them an important feature in quantum information theory. In this work, we propose the concept of virtual quantum Markov chains (VQMCs), focusing on scenarios where subsystems retain classical information about global systems from measurement statistics. As a generalization of quantum Markov chains, VQMCs characterize states where arbitrary global shadow information can be recovered from subsystems through local quantum operations and measurements. We present an algebraic characterization for virtual quantum Markov chains and show that the virtual quantum recovery is fully determined by the block matrices of a quantum state on its subsystems. Notably, we find a distinction between two classes of tripartite entanglement by showing that the W state is a VQMC while the GHZ state is not. Furthermore, we introduce the virtual non-Markovianity to quantify the non-Markovianity of a given quantum state, which also assesses the optimal sampling overhead for virtually recovering this state. Our findings elucidate distinctions between quantum Markov chains and virtual quantum Markov chains, extending our understanding of quantum recovery to scenarios prioritizing classical information from measurement statistics.