pith. sign in

arxiv: 2410.08937 · v3 · submitted 2024-10-11 · 🪐 quant-ph · cs.IT· math.IT

Distributed Quantum Hypothesis Testing under Zero-rate Communication Constraints

Pith reviewed 2026-05-23 19:09 UTC · model grok-4.3

classification 🪐 quant-ph cs.ITmath.IT
keywords distributed quantum hypothesis testingStein's exponentzero-rate communicationsingle-letter formulareverse hypercontractivityquantum Markov semigroupmeasured relative entropypinching method
0
0 comments X

The pith

When the alternative hypothesis is a product state, the Stein exponent in zero-rate distributed quantum hypothesis testing reduces to an efficiently computable single-letter expression.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper studies binary hypothesis testing for a bipartite quantum state shared between two parties, with one party sending information to a central tester at asymptotic zero rate and the other at zero or higher rate. It establishes a single-letter formula for the Stein exponent precisely when the alternative hypothesis equals the product of the two marginal states. The formula is derived via a new converse argument that combines reverse hypercontractivity of a quantum Markov semigroup with the pinching method. This supplies a concrete, computable characterization in a distributed setting where centralized results no longer apply directly.

Core claim

Under the product-state assumption for the alternative hypothesis, the Stein exponent of the zero-rate distributed quantum hypothesis testing problem equals an efficiently computable single-letter expression; the converse is proved by a combination of reverse hypercontractivity for a quantum Markov semigroup and pinching, while the direct part follows from standard techniques. For vanishing type-I error the exponent is given by a regularized measured-relative-entropy expression maximized over a subclass of binary-outcome separable measurements. When the alternative commutes with the product of the marginals and has larger support, the exponent is a max-min optimization of regularized-measur​

What carries the argument

Single-letter formula for the Stein exponent under the product marginals assumption, obtained via reverse hypercontractivity of a quantum Markov semigroup combined with pinching.

If this is right

  • The Stein exponent becomes efficiently computable rather than requiring regularization.
  • When type-I error vanishes, the exponent is expressed via regularized measured relative entropy over separable binary measurements.
  • For states whose alternative commutes with the marginal product, the exponent is a max-min optimization over local projective measurements.
  • In the fully classical case the expression collapses to single-letter form, but this collapse fails for general classical-quantum states.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The result suggests that similar single-letter simplifications may exist for other zero-rate distributed tasks when states factorize.
  • The extension of the blowing-up lemma to commuting bipartite quantum states may apply to other distributed quantum information problems.
  • Numerical evaluation of the single-letter formula could be compared against multi-letter expressions for small systems to quantify the gap when the product assumption is relaxed.

Load-bearing premise

The alternative hypothesis state is exactly the product of the two marginal states.

What would settle it

A concrete bipartite state and explicit rate pair for which the computed Stein exponent under product marginals differs numerically from the proposed single-letter expression.

Figures

Figures reproduced from arXiv: 2410.08937 by Christoph Hirche, Hao-Chung Cheng, Mario Berta, Sreejith Sreekumar.

Figure 1
Figure 1. Figure 1: Distributed quantum hypothesis testing under a zero-rate noiseless communication constraint. At least, one of [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
read the original abstract

The trade-offs between error probabilities in quantum hypothesis testing are by now well-understood in the centralized setting, but much less is known for distributed settings. Here, we study a distributed binary hypothesis testing problem to infer a bipartite quantum state shared between two remote parties, where one of these parties communicates to the tester at (asymptotic) zero-rate, while the other party communicates to the tester at zero-rate or higher. As our main contribution, we derive an efficiently computable single-letter formula for the Stein's exponent of this problem, when the state under the alternative is the product of their marginals. For proving the converse direction of our result, we utilize a novel technique based on reverse hypercontractivity of a quantum markov semigroup combined with the pinching method. For the general case with vanishing type I error probability, we show that the Stein's exponent when (at least) one of the parties communicates classically at zero-rate is given by a multi-letter expression involving regularized measured relative entropy maximized over a sub-class of binary outcome separable measurements. When the state under the alternative commutes with the product of marginals under the null and has a larger support, we show that the exponent is characterized as a max-min optimization of regularized measured relative entropy over a class of local binary outcome projective measurements. While this expression becomes single-letter for the fully classical case, we further prove that this already does not happen in the same way for classical-quantum states in general. The converse proof of the max-min characterization relies on an extension of the classical blowing-up lemma to bipartite quantum states whose marginals commute, which could be of independent interest.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The manuscript studies distributed binary quantum hypothesis testing for a shared bipartite state, where at least one party communicates to the tester at zero asymptotic rate. The central claim is an efficiently computable single-letter expression for the Stein exponent that holds precisely when the alternative hypothesis is the product of the two marginal states; the converse is proved via reverse hypercontractivity of a quantum Markov semigroup combined with pinching. For the general (non-product) case the exponent is characterized by a regularized multi-letter expression involving measured relative entropy maximized over a subclass of binary-outcome separable measurements. Special cases are treated when the alternative commutes with the product of marginals (yielding a max-min form over local projective measurements) and when the states are classical or classical-quantum.

Significance. If the derivations hold, the work supplies the first single-letter, efficiently computable Stein exponent for a nontrivial distributed quantum hypothesis-testing setting under zero-rate constraints. The restriction to the product alternative is explicitly stated, so the result is not over-claimed. The combination of reverse hypercontractivity with pinching and the extension of the classical blowing-up lemma to commuting-marginal bipartite states are technically novel and may find use beyond this problem. These contributions advance the understanding of distributed quantum information tasks beyond the fully regularized expressions that have been typical.

minor comments (3)
  1. [Abstract] Abstract, contribution paragraph: the single-letter formula is stated to hold 'when the state under the alternative is the product of their marginals'; the introduction should repeat this scope condition verbatim and contrast it immediately with the multi-letter general-case result.
  2. [Abstract] The phrase 'efficiently computable' is used for the single-letter formula; a brief remark (or reference to a known algorithm) on the complexity of evaluating the measured relative entropy or the optimization over binary-outcome measurements would strengthen the claim.
  3. [Abstract] The extension of the blowing-up lemma is announced as potentially of independent interest; a short self-contained statement of the lemma (even in an appendix) would make the claim easier to verify.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive and accurate summary of our manuscript, as well as for recommending minor revision. No specific major comments were raised in the report.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The claimed single-letter Stein exponent holds specifically under the product-of-marginals assumption on the alternative hypothesis and is derived via reverse hypercontractivity of a quantum Markov semigroup plus pinching (converse) together with standard quantum hypothesis testing tools (achievability). The general non-product case is left as an explicit multi-letter regularized expression; no step reduces by definition or self-citation to its own input. The result is self-contained against external benchmarks and does not rely on load-bearing self-citations or fitted quantities renamed as predictions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities can be extracted. The product-marginal assumption for the alternative hypothesis is the main structural premise identified.

pith-pipeline@v0.9.0 · 5837 in / 1173 out tokens · 17300 ms · 2026-05-23T19:09:05.450127+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

72 extracted references · 72 canonical work pages · 3 internal anchors

  1. [1]

    The proper formula for relative entropy and its asymptotics in quantum probability,

    F. Hiai and D. Petz, “The proper formula for relative entropy and its asymptotics in quantum probability,” Commun. Math. Phys. , vol. 143, no. 1, pp. 99 – 114, 1991

  2. [2]

    The Chernoff lower bound for symmetric quantum hypothesis testing,

    M. Nussbaum and A. Szkola, “The Chernoff lower bound for symmetric quantum hypothesis testing,” Ann. Stat., vol. 37, 08 2006

  3. [3]

    Asymptotic error rates in quantum hypothesis testing,

    Audenaert, KMR and Nussbaum, M and Szkola, A and Verstraete, Frank, “Asymptotic error rates in quantum hypothesis testing,” Commun. Math. Phys., vol. 279, no. 1, pp. 251–283, 2008

  4. [4]

    Conditional expectations in an operator algebra IV (entropy and information),

    H. Umegaki, “Conditional expectations in an operator algebra IV (entropy and information),” Kodai Math. Seminar Rep. , vol. 14, no. 2, pp. 59–85, 1962

  5. [5]

    On measures of entropy and information,

    A. R ´enyi, “On measures of entropy and information,” in Proc. 4th Berkeley Symp. Math. Stat. Probab. , vol. 1. Berkeley: University of California Press, 1961, pp. 547–561

  6. [6]

    Quasi-entropies for states of a von Neumann algebra,

    D. Petz, “Quasi-entropies for states of a von Neumann algebra,” Publ. Res. Inst. Math. Sci. , vol. 21, no. 4, pp. 787–800, Aug. 1985

  7. [7]

    Quasi-entropies for finite quantum systems,

    ——, “Quasi-entropies for finite quantum systems,” Rep. Math. Phys. , vol. 23, no. 1, pp. 57–65, Feb. 1986

  8. [8]

    On quantum R ´enyi entropies: A new generalization and some properties,

    M. M ¨uller-Lennert, F. Dupuis, O. Szehr, S. Fehr, and M. Tomamichel, “On quantum R ´enyi entropies: A new generalization and some properties,” J. Math. Phys. , vol. 54, no. 12, 2013

  9. [9]

    Strong converse for the classical capacity of entanglement-breaking and Hadamard channels via a sandwiched R ´enyi relative entropy,

    M. M. Wilde, A. Winter, and D. Yang, “Strong converse for the classical capacity of entanglement-breaking and Hadamard channels via a sandwiched R ´enyi relative entropy,” Commun. Math. Phys. , vol. 331, pp. 593–622, 2014

  10. [10]

    Hypothesis testing with multiterminal data compression,

    T. S. Han, “Hypothesis testing with multiterminal data compression,” IEEE Trans. Inf. Theory , vol. 33, no. 6, pp. 759–772, Nov. 1987

  11. [11]

    Multiterminal detection with zero-rate data compression,

    H. M. H. Shalaby and A. Papamarcou, “Multiterminal detection with zero-rate data compression,” IEEE Trans. Inf. Theory, vol. 38, no. 2, pp. 254–267, Mar. 1992

  12. [12]

    Bounds on conditional probabilities with applications in multi-user communication,

    R. Ahlswede, P. Gacs, and J. K ¨orner, “Bounds on conditional probabilities with applications in multi-user communication,” Zeitschrift f ¨ur Wahrscheinlichkeitstheorie und verwandte Gebiete, 34(2), pp. 157-177 , vol. 34, 01 1976

  13. [13]

    Csisz ´ar and J

    I. Csisz ´ar and J. K ¨orner, Information Theory: Coding Theorems for Discrete Memoryless Systems . Cambridge University Press, 2011

  14. [14]

    A simple proof of the blowing-up lemma (corresp.),

    K. Marton, “A simple proof of the blowing-up lemma (corresp.),” IEEE Trans. Inf. Theory , vol. 32, no. 3, pp. 445–446, 1986

  15. [15]

    Optimal sequence of quantum measurements in the sense of Stein’s lemma in quantum hypothesis testing,

    M. Hayashi, “Optimal sequence of quantum measurements in the sense of Stein’s lemma in quantum hypothesis testing,” J. Phys. A: Math. Gen., vol. 35, no. 50, p. 10759, Dec. 2002

  16. [16]

    Hypothesis testing with communication constraints,

    R. Ahlswede and I. Csisz ´ar, “Hypothesis testing with communication constraints,” IEEE Trans. Inf. Theory , vol. 32, no. 4, pp. 533–542, Jul. 1986

  17. [17]

    Error bound of hypothesis testing with data compression,

    H. Shimokawa, T. S. Han, and S. Amari, “Error bound of hypothesis testing with data compression,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Trondheim, Norway, Jun.-Jul. 1994, pp. 114–114

  18. [18]

    On sub-optimality of random binning for distributed hypothesis testing,

    S. Watanabe, “On sub-optimality of random binning for distributed hypothesis testing,” in 2022 IEEE Int. Symp. Inf. Theory (ISIT) , 2022, pp. 2708–2713

  19. [19]

    Improved random-binning exponent for distributed hypothesis testing,

    Y . Kochman and L. Wang, “Improved random-binning exponent for distributed hypothesis testing,” arXiv:2306.14499 [cs.IT], 2023

  20. [20]

    On the optimality of binning for distributed hypothesis testing,

    M. S. Rahman and A. B. Wagner, “On the optimality of binning for distributed hypothesis testing,” IEEE Trans. Inf. Theory , vol. 58, no. 10, pp. 6282–6303, Oct. 2012. 35

  21. [21]

    Error exponents in distributed hypothesis testing of correlations,

    U. Hadar, J. Liu, Y . Polyanskiy, and O. Shayevitz, “Error exponents in distributed hypothesis testing of correlations,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT) , Paris, France, Jul. 2019, pp. 2674–2678

  22. [22]

    An improved upper bound for distributed hypothesis testing,

    Y . Kochman, “An improved upper bound for distributed hypothesis testing,” in 2024 IEEE Int. Symp. Inf. Theory (ISIT) , 2024, pp. 2909– 2914

  23. [23]

    Successive refinement for hypothesis testing and lossless one-helper problem,

    C. Tian and J. Chen, “Successive refinement for hypothesis testing and lossless one-helper problem,” IEEE Trans. Inf. Theory , vol. 54, no. 10, pp. 4666–4681, Oct. 2008

  24. [24]

    Interactive hypothesis testing against independence,

    Y . Xiang and Y . H. Kim, “Interactive hypothesis testing against independence,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT) , Istanbul, Turkey, Nov. 2013, pp. 2840–2844

  25. [25]

    Collaborative Distributed Hypothesis Testing

    G. Katz, P. Piantanida, and M. Debbah, “Collaborative distributed hypothesis testing,” arXiv:1604.01292 [cs.IT], Apr. 2016

  26. [26]

    Distributed hypothesis testing over noisy channels,

    S. Sreekumar and D. G ¨und¨uz, “Distributed hypothesis testing over noisy channels,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT) , Aachen, Germany, Jun. 2017

  27. [27]

    Strong converse for testing against independence over a noisy channel,

    S. Sreekumar and D. G ´und´uz, “Strong converse for testing against independence over a noisy channel,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Los Angeles, CA, USA, Jun. 2020, pp. 1283–1288

  28. [28]

    Distributed hypothesis testing over discrete memoryless channels,

    S. Sreekumar and D. G ¨und¨uz, “Distributed hypothesis testing over discrete memoryless channels,” IEEE Trans. Inf. Theory, vol. 66, no. 4, pp. 2044–2066, Apr. 2020

  29. [29]

    Distributed hypothesis testing based on unequal-error protection codes,

    S. Salehkalaibar and M. Wigger, “Distributed hypothesis testing based on unequal-error protection codes,” IEEE Trans. Inf. Theory, vol. 66, no. 7, pp. 4150–4182, Jul. 2020

  30. [30]

    Distributed testing against independence with multiple terminals,

    W. Zhao and L. Lai, “Distributed testing against independence with multiple terminals,” in Proc. 52nd Annu. Allerton Conf. Commun. Control Comput., Monticello, IL, USA, Sep.-Oct. 2014, pp. 1246–1251

  31. [31]

    Testing against independence with multiple decision centers,

    M. Wigger and R. Timo, “Testing against independence with multiple decision centers,” in Proc. Int. Conf. Signal Proc. Commun. , Bangalore, India, Jun. 2016, pp. 1–5

  32. [32]

    Distributed testing with cascaded encoders,

    W. Zhao and L. Lai, “Distributed testing with cascaded encoders,” IEEE Trans. Inf. Theory , vol. 64, no. 11, pp. 7339–7348, Nov. 2018

  33. [33]

    Hypothesis testing in multi-hop networks,

    S. Salehkalaibar, M. Wigger, and L. Wang, “Hypothesis testing in multi-hop networks,” IEEE Trans. Inf. Theory , vol. 65, no. 7, pp. 4411–4433, Jul. 2019

  34. [34]

    Distributed hypothesis testing: Cooperation and concurrent detection,

    P. Escamilla, M. Wigger, and A. Zaidi, “Distributed hypothesis testing: Cooperation and concurrent detection,” IEEE Trans. Inf. Theory , vol. 66, no. 12, pp. 7550–7564, 2020

  35. [35]

    A fundamental limit of distributed hypothesis testing under memoryless quantization,

    Y . ˙Inan, M. Kayaalp, A. H. Sayed, and E. Telatar, “A fundamental limit of distributed hypothesis testing under memoryless quantization,” in IEEE Int. Conf. Commun. , 2022, pp. 4824–4829

  36. [36]

    Rate-exponent region for a class of distributed hypothesis testing against conditional independence problems,

    A. Zaidi, “Rate-exponent region for a class of distributed hypothesis testing against conditional independence problems,” IEEE Trans. Inf. Theory, vol. 69, no. 2, pp. 703–718, 2023

  37. [37]

    Multi-hop network with multiple decision centers under expected-rate constraints,

    M. Hamad, M. Wigger, and M. Sarkiss, “Multi-hop network with multiple decision centers under expected-rate constraints,” IEEE Trans. Inf. Theory, vol. 69, no. 7, pp. 4255–4283, 2023

  38. [38]

    Exponential-type error probabilities for multiterminal hypothesis testing,

    T. S. Han and K. Kobayashi, “Exponential-type error probabilities for multiterminal hypothesis testing,” IEEE Trans. Inf. Theory , vol. 35, no. 1, pp. 2–14, Jan. 1989

  39. [39]

    Statistical inference under multiterminal data compression,

    T. S. Han and S. Amari, “Statistical inference under multiterminal data compression,” IEEE Trans. Inf. Theory, vol. 44, no. 6, pp. 2300–2324, Oct. 1998

  40. [40]

    Distributed testing with zero-rate compression,

    W. Zhao and L. Lai, “Distributed testing with zero-rate compression,” in 2015 IEEE Int. Symp. Inf. Theory (ISIT) , 2015, pp. 2792–2796

  41. [41]

    Binary distributed hypothesis testing via K ¨orner-Marton coding,

    E. Haim and Y . Kochman, “Binary distributed hypothesis testing via K ¨orner-Marton coding,” in 2016 IEEE Inf. Theory Workshop (ITW) , 2016, pp. 146–150

  42. [42]

    Neyman-Pearson test for zero-rate multiterminal hypothesis testing,

    S. Watanabe, “Neyman-Pearson test for zero-rate multiterminal hypothesis testing,” IEEE Trans. Inf. Theory, vol. 64, no. 7, pp. 4923–4939, Jul. 2018

  43. [43]

    On the reliability function of distributed hypothesis testing under optimal detection,

    N. Weinberger and Y . Kochman, “On the reliability function of distributed hypothesis testing under optimal detection,” IEEE Trans. Inf. Theory, vol. 65, no. 8, pp. 4940–4965, Apr. 2019

  44. [44]

    Exponent trade-off for hypothesis testing over noisy channels,

    N. Weinberger, Y . Kochman, and M. Wigger, “Exponent trade-off for hypothesis testing over noisy channels,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT) , Paris, France, 2019, pp. 1852–1856

  45. [45]

    On distributed learning with constant communication bits,

    X. Xu and S.-L. Huang, “On distributed learning with constant communication bits,” IEEE J. Sel. Areas Inf. Theory , vol. 3, no. 1, pp. 125–134, Mar. 2022

  46. [46]

    Distributed hypothesis testing over a noisy channel: Error-exponents trade-off,

    S. Sreekumar and D. G ¨und¨uz, “Distributed hypothesis testing over a noisy channel: Error-exponents trade-off,” Entropy, vol. 25, no. 2, 2023. 36

  47. [47]

    Distributed binary detection with lossy data compression,

    G. Katz, P. Piantanida, and M. Debbah, “Distributed binary detection with lossy data compression,” IEEE Trans. Inf. Theory , vol. 63, no. 8, pp. 5207–5227, Mar. 2017

  48. [48]

    On secure distributed hypothesis testing,

    M. Mhanna and P. Piantanida, “On secure distributed hypothesis testing,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT) , Hong Kong, China, Jun. 2015, pp. 1605–1609

  49. [49]

    Distributed hypothesis testing under privacy constraints,

    S. Sreekumar, D. G ¨und¨uz, and A. Cohen, “Distributed hypothesis testing under privacy constraints,” in Proc. IEEE Inf. Theory Workshop (ITW), 2018, pp. 1–5

  50. [50]

    Distributed hypothesis testing with privacy constraints,

    A. Gilani, S. B. Amor, S. Salehkalaibar, and V . Tan, “Distributed hypothesis testing with privacy constraints,” Entropy, vol. 21, no. 478, pp. 1–27, May 2019

  51. [51]

    Privacy-aware distributed hypothesis testing,

    S. Sreekumar, A. Cohen, and D. G ¨und¨uz, “Privacy-aware distributed hypothesis testing,” Entropy, vol. 22, no. 6, Jun. 2020

  52. [52]

    Testing against conditional independence under security constraints,

    S. Sreekumar and D. G ¨und¨uz, “Testing against conditional independence under security constraints,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Vail, CO, USA, Jun. 2018, pp. 181–185

  53. [53]

    Strong converse bounds in quantum network information theory,

    H.-C. Cheng, N. Datta, and C. Rouz ´e, “Strong converse bounds in quantum network information theory,” IEEE Trans. Inf. Theory, vol. 67, no. 4, pp. 2269–2292, 2021

  54. [54]

    On variational expressions for quantum relative entropies,

    M. Berta, O. Fawzi, and M. Tomamichel, “On variational expressions for quantum relative entropies,” Lett. Math. Phys. , vol. 107, no. 12, pp. 2239–2265, Dec. 2015

  55. [55]

    Hypothesis testing over a noisy channel,

    S. Sreekumar and D. G ¨und¨uz, “Hypothesis testing over a noisy channel,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT) , Paris, France, 2019

  56. [56]

    Distributed sequential hypothesis testing with zero-rate compression,

    S. Salehkalaibar and V . Y . F. Tan, “Distributed sequential hypothesis testing with zero-rate compression,” in Proc. 2021 IEEE Inf. Theory Workshop (ITW), Kanazawa, Japan, 2021, pp. 1–5

  57. [57]

    Correlation detection and an operational interpretation of the R ´enyi mutual information,

    M. Hayashi and M. Tomamichel, “Correlation detection and an operational interpretation of the R ´enyi mutual information,” J. Math. Phys., vol. 57, no. 10, p. 102201, 10 2016

  58. [58]

    On composite quantum hypothesis testing,

    M. Berta, F. Brandao, and C. Hirche, “On composite quantum hypothesis testing,” Commun. Math. Phys. , vol. 385, 07 2021

  59. [59]

    Quantum states with Einstein-Podolsky-Rosen correlations admitting a hidden-variable model,

    R. F. Werner, “Quantum states with Einstein-Podolsky-Rosen correlations admitting a hidden-variable model,” Phys. Rev. A, vol. 40, no. 8, pp. 4277–4281, 1989

  60. [60]

    Locally-measured R ´enyi divergences,

    T. Rippchen, S. Sreekumar, and M. Berta, “Locally-measured R ´enyi divergences,” arXiv:2405.05037 [quant-ph], 2024

  61. [61]

    Means of positive linear operators

    F. Kubo and T. Ando, “Means of positive linear operators.” Math. Ann., vol. 246, pp. 205–224, 1979

  62. [62]

    T. M. Cover and J. A. Thomas, Elements of Information Theory . NewYork: Wiley, 1991

  63. [63]

    Tomamichel, Quantum Information Processing with Finite Resources: Mathematical Foundations

    M. Tomamichel, Quantum Information Processing with Finite Resources: Mathematical Foundations . Springer, 2015

  64. [64]

    Concentration of measure inequalities in information theory, communications, and coding,

    M. Raginsky and I. Sason, “Concentration of measure inequalities in information theory, communications, and coding,” Foundations and Trends® in Communications and Information Theory , vol. 10, no. 1-2, pp. 1–246, 2013

  65. [65]

    Networked quantum sensing

    T. J. Proctor, P. A. Knott, and J. A. Dunningham, “Networked quantum sensing,” arXiv:1702.04271 [quant-ph], 2017

  66. [66]

    Quantum sensor network algorithms for transmitter localization,

    C. Zhan and H. Gupta, “Quantum sensor network algorithms for transmitter localization,” in 2023 IEEE Int. Conf. Quantum Comput. Eng. (QCE). Los Alamitos, CA, USA: IEEE Computer Society, Sep. 2023, pp. 659–669

  67. [67]

    Quantum-based wireless sensor networks: A review and open questions,

    M. Rivero-Angeles, “Quantum-based wireless sensor networks: A review and open questions,” Int. J. Distrib. Sens. Netw. , vol. 17, Oct. 2021

  68. [68]

    Quantum-secure covert communication on bosonic channels,

    B. Bash, A. Gheorghe, M. Patel, J. Habif, D. Goeckel, D. Towsley, and S. Guha, “Quantum-secure covert communication on bosonic channels,” Nat. Commun., vol. 6, 10 2015

  69. [69]

    Limits of reliable communication with low probability of detection on AWGN channels,

    B. A. Bash, D. Goeckel, and D. Towsley, “Limits of reliable communication with low probability of detection on AWGN channels,” IEEE J. Sel. Areas Commun. , vol. 31, no. 9, pp. 1921–1930, 2013

  70. [70]

    Fundamental limits of communication with low probability of detection,

    L. Wang, G. W. Wornell, and L. Zheng, “Fundamental limits of communication with low probability of detection,” IEEE Trans. Inf. Theory, vol. 62, no. 6, pp. 3493–3503, 2016

  71. [71]

    Covert communication over noisy channels: A resolvability perspective,

    M. Bloch, “Covert communication over noisy channels: A resolvability perspective,” IEEE Trans. Inf. Theory, vol. 62, no. 5, pp. 2334–2354, 2016

  72. [72]

    Quantum Stein's lemma revisited, inequalities for quantum entropies, and a concavity theorem of Lieb

    I. Bjelakovic and R. Siegmund-Schultze, “Quantum Stein’s lemma revisited, inequalities for quantum entropies, and a concavity theorem of Lieb,” arXiv:quant-ph/0307170v2, 2012