Fundamental Limits of Covert Communication over Classical-Quantum Channels
Michael S. Bullock, Azadeh Sheikholeslami, Mehrdad Tahmasbi, Robert C., Macdonald, Saikat Guha, Boulat A. Bash

TL;DR
This paper establishes the fundamental limits of covert communication over classical-quantum channels, demonstrating the square root law governs the maximum covert bits transmitted and the pre-shared secret requirements, with explicit formulas for channel-dependent constants.
Contribution
It derives single-letter expressions for the covert capacity and secret key requirements in classical-quantum channels, extending the understanding of covert communication limits under quantum adversaries.
Findings
Covert communication follows the square root law with a channel-dependent constant.
Pre-shared secret bits scale with the square root of the number of channel uses.
Conditions are identified when no pre-shared secret is necessary.
Abstract
We investigate covert communication over general memoryless classical-quantum channels with fixed finite-size input alphabets. We show that the square root law (SRL) governs covert communication in this setting when product of input states is used: covert bits (but no more) can be reliably transmitted in uses of classical-quantum channel, where is a channel-dependent constant that we call covert capacity. We also show that ensuring covertness requires bits secret shared by the communicating parties prior to transmission, where is a channel-dependent constant. We assume a quantum-powerful adversary that can perform an arbitrary joint (entangling) measurement on all channel uses. We determine the single-letter expressions for and , and establish…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDeception detection and forensic psychology · Internet Traffic Analysis and Secure E-voting · Adversarial Robustness in Machine Learning
