Abstract
Continuous-variable quantum communication depends on finite-window estimates of quadrature statistics, so receiver decisions are inherently shaped by calibration error, measurement resolution, and confidence-based acceptance limits. This work presents a receiver-observable framework for analyzing structured physical-layer disturbances, including malicious interference, under realistic finite-sample conditions. Rather than classifying events by presumed attack mechanism, the approach organizes them by statistical visibility at the receiver, distinguishing stealthy reconnaissance activity, exploratory stress-inducing perturbations, and overt denial-of-service behavior. An effective Gaussian estimator-space representation is used to describe how different disturbances can produce operationally similar covariance signatures within tolerance bands. To compare severity across implementations, the study introduces a protocol-independent energy-deviation indicator derived from the covariance trace. Closed-form missed-detection relationships based on finite-sample testing show how detectability improves with block length, while Monte Carlo results confirm the predicted soft transition from statistically hidden disturbances to clearly resolvable deviations. The resulting framework offers a practical basis for monitoring-layer design, threat categorization, and comparative analysis in continuous-variable quantum networks and related communication platforms.
Biography
José is an innovative leader in decentralized technologies, quantum computing (QC), and cybersecurity, with over two decades of experience in IT. He specializes in national security and defense and holds significant intellectual property in IT and quantum computing, including patents and industrial secrets. He is currently pursuing a Ph.D. in post-quantum cryptography and QC at the University of Waterloo (Canada) and contributes to projects including QRNG networks, decentralized quantum networks (DQN), and quantum adaptive multifrequency defense systems (QAMDS).
