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Jianxin Song

Publications and source records attributed to Jianxin Song.

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Unified Framework for Quantum Resource Recycling via Instrument-Dependent Back-action

Accurate characterization of measurement backaction is crucial for understanding the limits of reusing quantum correlations in sequential scenarios. Here, we develop a unified quantum-instrument framework that goes beyond simple measurement statistics, explicitly attributing correlation sharing to Kraus-structure-dependent backaction. By tracing operational differences to this underlying physical mechanism, our framework integrates previously disparate strategies. Within this formulation, we derive general conditions for unbounded unilateral nonlocality sharing across arbitrarily many observers. The framework further reveals that Bell nonlocality remains shareable in bilateral sequential scenarios. These results establish quantum instruments, rather than POVMs alone, as the fundamental constraints on correlation sharing, providing a unified conceptual framework for quantum resource recycling.

quant-ph

Efficient Identification the Inequivalence of Mutually Unbiased Bases via Finite Operators

The structural characterization of high-dimensional mutually unbiased bases (MUBs) by classifying MUBs subsets remains a major open problem. The existing methods not only fail to conclude on the exact classification, but also are severely limited by computational resources and suffer from the numerical precision problem. Here we introduce an operational approach to identify the inequivalence of MUBs subsets, which has less time complexity and entirely avoids the computational precision issues. For arbitrary MUBs subsets of $k$ elements in any prime dimension, this method yields a universal analytical upper bound for the amount of MUBs equivalence classes. By applying this method through simple iterations, we further obtain tighter classification upper bounds for any prime dimension $d\leq 37$. Crucially, the comparison of these upper bounds with existing lower bounds successfully determines the exact classification for all MUBs subsets in any dimension $d \leq 17$. We further extend this method to the case that the dimension is a power of prime number. This general and scalable framework for the classification of MUBs subsets sheds new light on related applications.

quant-ph