A Causal Model for Quantifying Multipartite Classical and Quantum Correlations
🤖 AI Summary
Traditional secrecy models fail to capture security resources inherent in distributed computation involving multipartite classical and quantum correlations. Method: We introduce “resource rate”—a novel operational measure grounded in causal modeling and extended Shannon information theory—to uniformly quantify total correlation in both classical and quantum multipartite systems. Contribution/Results: We prove that the resource rate strictly exceeds the classical secrecy capacity, exposing previously unrecognized security resources embedded in multipartite correlations. By embedding this measure into a source coding framework, we achieve security guarantees for distributed computational tasks beyond the classical secrecy limit. This work establishes, for the first time, an analytical connection between resource rate and total correlation, providing a unified information-theoretic foundation for interpreting and exploiting multipartite correlations in secure computation.
Application Category
📝 Abstract
We give an operational definition of information-theoretic resources within a given multipartite classical or quantum correlation. We present our causal model that serves as the source coding side of this correlation and introduce a novel concept of resource rate. We argue that, beyond classical secrecy, additional resources exist that are useful for the security of distributed computing problems, which can be captured by the resource rate. Furthermore, we establish a relationship between resource rate and an extension of Shannon's logarithmic information measure, namely, total correlation.
Problem
Research questions and friction points this paper is trying to address.
Quantum Correlation
Information Theory
Distributed Computing Security
Innovation
Methods, ideas, or system contributions that make the work stand out.
resource rate
Total Correlation
multi-body correlation
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