REVIEW 3 major objections 2 minor 38 references
From free-evolution to tomographic representation
T0 review · 3 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash
Pith's one-line read The paper claims that the free-evolution propagator alone determines the tomogram — the quantum probability representation — of any one-dimensional density state, including its time dependence.
desk verdict The advertised quantum paper is not in the submission—the full text is an unrelated hardware-security paper, so the tomogram claim cannot be checked. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central object is the free-evolution propagator, the kernel that maps the wavefunction at an initial time to its later values. The paper's method is to insert this propagator into the definition of the tomogram for a density state, producing the general tomogram $\omega(X,\mu,\nu)$ — the probability distribution of the quadrature $X=\mu x+\nu p$ — and its time-dependent form. The same propagator is the building block for the N-particle generalisation, and the linear entropy computed from the two-particle tomogram is the entanglement quantifier.
What would settle it
Take a superposition of two Gaussian wave packets as the initial state and compute the tomogram by the paper's propagator formula at a single later time. Compare it with the direct tomogram obtained from the Wigner function, $\omega(X,\mu,\nu)=\int W(x,p)\,\delta(X-\mu x-\nu p)\,dx\,dp$. Any disagreement for one such state and time falsifies the claimed universal expression.
Extended reading notes
Core claim
The central discovery asserted by the abstract is that the free-evolution propagator $K(x,t;x',t_0)$ determines the general tomographic probability distribution for any one-dimensional system described by a density state, and that the same propagator yields the time-dependent tomogram. This is stated as a general expression, with the Gaussian wave packet, quantum shutter, double quantum shutter, and finite potential serving as applications. The generalization to N particles follows from the N-particle free propagator; for the two-particle case with non-orthogonal states the paper obtains the tomogram and characterises entanglement through the linear entropy. As submitted, the full text attac
Load-bearing premise
The central claim collapses if the free-evolution propagator does not, by itself, determine the tomogram of every one-dimensional density state; it also presupposes that the submitted full text contains the derivation, and the supplied text is an unrelated paper, so that derivation is absent.
Editorial extensions
If this is right
- If the central claim is correct, the tomogram of any one-dimensional density state follows directly from the propagator and the initial state, without a separate tomographic-inversion step.
- The worked examples become explicit tomograms that can be compared with measured probabilities: the Gaussian wave packet, the quantum shutter (diffraction in time), the double quantum shutter, and the finite potential.
- The N-particle generalisation extends the propagator-to-tomogram route to multipartite systems, and the two-particle case gives a linear-entropy characterisation of entanglement for non-orthogonal states.
Reading between the lines
- The attached full text is a different submission, 'Designing with Deception: ML- and Covert Gate-Enhanced Camouflaging to Thwart IC Reverse Engineering'; the tomogram derivation and its proof are not in the supplied document, so the public record currently cannot be checked beyond the abstract.
- If the propagator-to-tomogram identity is universal, continuous-variable state reconstruction could be reduced to knowing the propagator, which would simplify experimental characterisation of evolving quantum states.
- Using linear entropy for two particles in non-orthogonal states suggests a tomographic route to entanglement detection that avoids full density-matrix reconstruction; a natural test is comparing predicted linear-entropy values with tomograms measured on entangled continuous-variable states.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The submission is headed by an abstract that announces a quantum-information result: using the free-evolution propagator to obtain the general quantum probability representation (tomogram) of any one-dimensional density state, with time-dependent tomograms, worked examples (Gaussian wave packet, quantum shutter, double shutter, finite potential), an N-particle generalization, and a two-particle entanglement analysis via linear entropy. The body of the submission, however, is not the paper described by this abstract. The attached full text is arXiv:2508.08462v1 [cs.CR], 'Designing with Deception: ML- and Covert Gate-Enhanced Camouflaging to Thwart IC Reverse Engineering,' a hardware-security paper with no quantum content, no tomograms, no propagators, and no derivation of the advertised result. Consequently, the central claim of the abstract cannot be checked, confirmed, or refuted from the supplied document.
Significance. If the abstract's claim were substantiated, the paper would address a genuine question in the tomographic probability representation of quantum states: whether the free-evolution propagator alone determines the general tomogram for one-dimensional density states and extends to multipartite systems with entanglement characterization. Such a result would be of interest to the quantum-information and quantum-foundations communities. However, a significant assessment cannot be made on the submitted material. The submission contains no equations, no derivations, and no examples attributable to the advertised paper. There is also no reproducible code or machine-checked proof to provide independent support. The only verifiable content in the submission is a full-text hardware-security manuscript that is unrelated to the abstract, so the significance claim rests entirely on an unverifiable abstract.
major comments (3)
- [Full Text (entire document)] The submitted full text is arXiv:2508.08462v1 [cs.CR], 'Designing with Deception: ML- and Covert Gate-Enhanced Camouflaging to Thwart IC Reverse Engineering,' by Junling Fan, David Koblah, and Domenic Forte. This document contains no quantum-mechanical content, no propagators, no tomograms, and no equations relevant to the abstract's claim. The abstract for arXiv:2508.08456 states that the free-evolution propagator 'determines the quantum probability representation (i.e., the general expression of the tomogram) of any one-dimensional system described by a density state,' but the supplied manuscript provides no derivation of this statement. This is a load-bearing obstacle: the central claim is unverifiable from the submission, and there is no way to assess whether the claimed generality is justified or only illustrated by special cases.
- [Abstract, second sentence] The abstract states that the evolution operator is 'used to establish the corresponding time dependent tomogram.' No equation or argument in the supplied full text supports this temporal extension. Without the actual derivation, it is impossible to determine whether the time dependence follows from the free propagator or requires additional assumptions about the Hamiltonian or the chosen tomographic map.
- [Abstract, applications list] The abstract lists applications to a Gaussian wave packet, the quantum shutter, the double quantum shutter, and a finite potential, and later claims an N-particle generalization with a two-particle, non-orthogonal-state entanglement analysis via linear entropy. None of these applications or calculations appears in the supplied full text. The reader cannot verify whether these are worked examples of a universal formula or fits to a special construction. This absence is not a minor presentation issue; it removes the evidentiary basis for the paper's central claim.
minor comments (2)
- [General] The abstract does not define 'tomogram' or specify the tomographic map (e.g., symplectic tomography, spin tomography, or quadrature tomography). Even if the correct full text were supplied, this definitional precision would be needed to make the 'general expression' claim checkable.
- [Abstract, 'N particle systems'] The notation 'N particle systems' is used without specifying whether the particles are distinguishable, whether the state is pure or mixed, and what notion of tomogram is intended for multipartite systems. These details are required for the claimed generalization.
Circularity Check
No circularity established: the supplied full text is arXiv:2508.08462 (hardware security), not the quantum tomography paper; with no derivation present, no self-reduction can be quoted.
full rationale
The abstract describes a quantum result (free-evolution propagator determines tomograms), but the full text is actually "Designing with Deception: ML- and Covert Gate-Enhanced Camouflaging to Thwart IC Reverse Engineering" (arXiv:2508.08462v1 [cs.CR]). The supplied document contains no tomograms, propagators, density states, or equations from the abstract, so no derivation chain exists to walk. Under the requirement that circularity be demonstrated by quoting the specific reduction (e.g., Eq. X = Eq. Y by construction, or a fitted parameter renamed as prediction), no circular step can be identified from the available text. The mismatch is a serious submission-integrity / missing-support issue — the abstract's central claim is unverifiable against the attached document — but lack of verifiability is not itself circularity. The hardware-security text that is present does cite covert gates from [7], co-authored by D. Forte, as a building block; this is ordinary prior-work citation and the present method is benchmarked against [7] as a baseline rather than being equivalent to it by construction. Therefore the circularity score is 0, with no circular steps listed.
Assumptions & free parameters
assumptions (3)
- domain assumption The tomogram (probability representation) is a complete description of the quantum state, so a general expression of it is equivalent to knowing the state itself for any one-dimensional density state.
- standard math Time evolution is governed by a standard free-evolution propagator, i.e., the Hamiltonian and its unitary evolution are taken as given.
- domain assumption For the bipartite case, linear entropy computed from the reduced 2-particle state quantifies entanglement.
Cite this review
Pith. "Pith review of From free-evolution to tomographic representation." pith.science (2026). https://pith.science/paper/35HVZYLX
@misc{pith2026250808456,
author = {Pith},
title = {Pith review of: From free-evolution to tomographic representation},
year = {2026},
howpublished = {\url{https://pith.science/paper/35HVZYLX}},
note = {Machine review of arXiv:2508.08456}
}
abstract
We use the free evolution propagator to determine the quantum probability representation (i.e., the general expression of the tomogram) of any one-dimensional system described by a density state. The evolution operator for the considered quantum system is additionally used to establish the corresponding time dependent tomogram. Applications are given for a Gaussian wave packet, the quantum shutter related with the phenomenon of diffraction in time, the double quantum shutter, and a finite potential. A generalisation to describe $N$ particle systems is also presented and, in particular, we find the tomogram associated to the 2 particle case occupying in general non-orthogonal states. In the latter case, for a bipartite quantum system, the entanglement properties are established by considering quantum information concepts such as the linear entropy.
Reference graph
Works this paper leans on
-
[1]
A survey on chip to system reverse engineering,
S. E. Quadir, J. Chen, D. Forte, N. Asadizanjani, S. Shahbazmohamadi, L. Wang, J. Chandy, and M. Tehranipoor, “A survey on chip to system reverse engineering,”ACM journal on emerging technologies in computing systems (JETC), vol. 13, no. 1, pp. 1–34, 2016
work page 2016
-
[2]
The state-of-the-art in semiconductor reverse engineering,
R. Torrance and D. James, “The state-of-the-art in semiconductor reverse engineering,” inProceedings of the 48th Design Automation Conference, 2011, pp. 333–338
work page 2011
-
[3]
Ip protection and supply chain security through logic obfuscation: A systematic overview,
K. Shamsi, M. Li, K. Plaks, S. Fazzari, D. Z. Pan, and Y . Jin, “Ip protection and supply chain security through logic obfuscation: A systematic overview,”ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 24, no. 6, pp. 1–36, 2019
work page 2019
-
[4]
Circuit camouflage integration for hardware ip protection,
R. P. Cocchi, J. P. Baukus, L. W. Chow, and B. J. Wang, “Circuit camouflage integration for hardware ip protection,” inProceedings of the 51st Annual Design Automation Conference, 2014, pp. 1–5
work page 2014
-
[5]
Security analysis of integrated circuit camouflaging,
J. Rajendran, M. Sam, O. Sinanoglu, and R. Karri, “Security analysis of integrated circuit camouflaging,” inProceedings of the 2013 ACM SIGSAC conference on Computer & communications security, 2013, pp. 709–720
work page 2013
-
[6]
A game-theoretic taxonomy and survey of defensive deception for cybersecurity and privacy,
J. Pawlick, E. Colbert, and Q. Zhu, “A game-theoretic taxonomy and survey of defensive deception for cybersecurity and privacy,”ACM Computing Surveys (CSUR), vol. 52, no. 4, pp. 1–28, 2019
2019
-
[7]
Covert gates: Protecting integrated circuits with undetectable camouflaging,
B. Shakya, H. Shen, M. Tehranipoor, and D. Forte, “Covert gates: Protecting integrated circuits with undetectable camouflaging,”IACR transactions on cryptographic hardware and embedded systems, pp. 86– 118, 2019
work page 2019
-
[8]
M. G. Rekoff, “On reverse engineering,”IEEE Transactions on systems, man, and cybernetics, no. 2, pp. 244–252, 1985
work page 1985
Show all 38 references
-
[9]
On reverse engineering-based hard- ware trojan detection,
C. Bao, D. Forte, and A. Srivastava, “On reverse engineering-based hard- ware trojan detection,”IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 35, no. 1, pp. 49–57, 2015
2015
-
[10]
A novel algorithm for hardware trojan detection through reverse engineering,
S. Rajendran and M. L. Regeena, “A novel algorithm for hardware trojan detection through reverse engineering,”IEEE Transactions on Computer- Aided Design of Integrated Circuits and Systems, vol. 41, no. 4, pp. 1154–1166, 2021
2021
-
[11]
The sat attack,
M. Yasin, J. Rajendran, O. Sinanoglu, M. Yasin, J. Rajendran, and O. Sinanoglu, “The sat attack,”Trustworthy Hardware Design: Com- binational Logic Locking Techniques, pp. 47–56, 2020
2020
-
[12]
The sat attack on ic camouflaging: Impact and potential countermeasures,
M. El Massad, S. Garg, and M. V . Tripunitara, “The sat attack on ic camouflaging: Impact and potential countermeasures,”IEEE Transac- tions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 8, pp. 1577–1590, 2019
2019
-
[13]
Cimsat: Exploiting sat analysis to attack compute-in-memory architecture defenses,
J. Wang, H. Yang, S. Deng, and X. Li, “Cimsat: Exploiting sat analysis to attack compute-in-memory architecture defenses,” inProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, 2024, pp. 3436–3450
2024
-
[14]
Cycsat: Sat-based attack on cyclic logic encryptions,
H. Zhou, R. Jiang, and S. Kong, “Cycsat: Sat-based attack on cyclic logic encryptions,” in2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). IEEE, 2017, pp. 49–56
2017
-
[15]
Sigattack: New high- level sat-based attack on logic encryptions,
Y . Shen, Y . Li, S. Kong, A. Rezaei, and H. Zhou, “Sigattack: New high- level sat-based attack on logic encryptions,” in2019 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2019, pp. 940–943
2019
-
[16]
The key is left under the mat: On the inappropriate security assumption of logic locking schemes,
M. T. Rahman, S. Tajik, M. S. Rahman, M. Tehranipoor, and N. Asadizanjani, “The key is left under the mat: On the inappropriate security assumption of logic locking schemes,” in2020 IEEE Interna- tional Symposium on Hardware Oriented Security and Trust (HOST). IEEE, 2020, pp. 262–272
2020
-
[17]
Laserescape: Detecting and mitigating optical probing attacks,
S. K. Monfared, K. Mitard, A. Cannon, D. Forte, and S. Tajik, “Laserescape: Detecting and mitigating optical probing attacks,”arXiv preprint arXiv:2405.03632, 2024
2024 arXiv
-
[18]
A secure camouflaged threshold voltage defined logic family,
B. Erbagci, C. Erbagci, N. E. C. Akkaya, and K. Mai, “A secure camouflaged threshold voltage defined logic family,” in2016 IEEE In- ternational symposium on hardware oriented security and trust (HOST). IEEE, 2016, pp. 229–235
2016
-
[19]
Development of a layout-level hardware obfuscation tool,
S. Malik, G. T. Becker, C. Paar, and W. P. Burleson, “Development of a layout-level hardware obfuscation tool,” in2015 IEEE computer society annual symposium on VLSI. IEEE, 2015, pp. 204–209
2015
-
[20]
Incremental sat-based reverse engineering of camouflaged logic circuits,
C. Yu, X. Zhang, D. Liu, M. Ciesielski, and D. Holcomb, “Incremental sat-based reverse engineering of camouflaged logic circuits,”IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 36, no. 10, pp. 1647–1659, 2017
2017
-
[21]
Auto-encoding variational bayes,
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,”arXiv preprint arXiv:1312.6114, 2013
2013 arXiv
-
[22]
The mnist database of handwritten digit images for machine learning research [best of the web],
L. Deng, “The mnist database of handwritten digit images for machine learning research [best of the web],”IEEE signal processing magazine, vol. 29, no. 6, pp. 141–142, 2012
2012
-
[23]
Vae explainer: Supplement learning variational autoencoders with interactive visualization,
D. Bertucci and A. Endert, “Vae explainer: Supplement learning variational autoencoders with interactive visualization,”arXiv preprint arXiv:2409.09011, 2024
2024 arXiv
-
[24]
Cyber deception: State of the art, trends and open challenges,
P. B. L ´opez, M. G. P ´erez, and P. Nespoli, “Cyber deception: State of the art, trends and open challenges,”arXiv preprint arXiv:2409.07194, 2024
2024 arXiv
-
[25]
Thwarting security threats from malicious fpga tools with novel fpga-oriented moving target defense,
Z. Zhang, L. Njilla, C. A. Kamhoua, and Q. Yu, “Thwarting security threats from malicious fpga tools with novel fpga-oriented moving target defense,”IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 27, no. 3, pp. 665–678, 2018
2018
-
[26]
Randohm: Mitigating impedance side-channel attacks using randomized circuit configurations,
S. K. Monfared, D. Forte, and S. Tajik, “Randohm: Mitigating impedance side-channel attacks using randomized circuit configurations,”arXiv preprint arXiv:2401.08925, 2024
2024 arXiv
-
[27]
Functionality matters in netlist representation learning,
Z. Wang, C. Bai, Z. He, G. Zhang, Q. Xu, T.-Y . Ho, B. Yu, and Y . Huang, “Functionality matters in netlist representation learning,” inProceedings of the 59th ACM/IEEE Design Automation Conference, 2022, pp. 61–66
2022
-
[28]
Graph learning- based arithmetic block identification,
Z. He, Z. Wang, C. Bai, H. Yang, and B. Yu, “Graph learning- based arithmetic block identification,” in2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 2021, pp. 1–8
2021
-
[29]
Variational graph auto-encoders,
T. N. Kipf and M. Welling, “Variational graph auto-encoders,”arXiv preprint arXiv:1611.07308, 2016
2016 arXiv
-
[30]
D-vae: A variational autoencoder for directed acyclic graphs,
M. Zhang, S. Jiang, Z. Cui, R. Garnett, and Y . Chen, “D-vae: A variational autoencoder for directed acyclic graphs,”arXiv preprint arXiv:1904.11088, 2019
1904 arXiv
-
[31]
Learning phrase representations using rnn encoder-decoder for statistical machine translation,
K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y . Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,”arXiv preprint arXiv:1406.1078, 2014. [Online]. Available: https://arxiv.org/abs/1406. 1078
2014 arXiv
-
[32]
Gnn-re: Graph neural networks for reverse engineering of gate-level netlists,
L. Alrahis, A. Sengupta, J. Knechtel, S. Patnaik, H. Saleh, B. Moham- mad, M. Al-Qutayri, and O. Sinanoglu, “Gnn-re: Graph neural networks for reverse engineering of gate-level netlists,”IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 41, no...
2022
-
[33]
Unveiling the iscas-85 benchmarks: A case study in reverse engineering,
M. C. Hansen, H. Yalcin, and J. P. Hayes, “Unveiling the iscas-85 benchmarks: A case study in reverse engineering,”IEEE Design & Test of Computers, vol. 16, no. 3, pp. 72–80, 1999
1999
-
[34]
The epfl combinational benchmark suite,
L. Amar ´u, P.-E. Gaillardon, and G. De Micheli, “The epfl combinational benchmark suite,” inProceedings of the 24th International Workshop on Logic & Synthesis (IWLS), 2015
2015
-
[35]
Deep graph library: A graph-centric, highly-performant package for graph neural networks,
M. Wang, D. Zheng, Z. Ye, Q. Gan, M. Li, X. Song, J. Zhou, C. Ma, L. Yu, Y . Gaiet al., “Deep graph library: A graph-centric, highly-performant package for graph neural networks,”arXiv preprint arXiv:1909.01315, 2019
1909 arXiv
-
[36]
Pytorch: An imperative style, high-performance deep learning library,
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antigaet al., “Pytorch: An imperative style, high-performance deep learning library,”Advances in neural information processing systems, vol. 32, 2019
2019
-
[37]
Exploring network structure, dynamics, and function using networkx,
A. Hagberg, P. Swart, and D. Chult, “Exploring network structure, dynamics, and function using networkx,” inProceedings of the 7th Python in Science Conference, 2008, pp. 11–15
2008
-
[38]
Graph- saint: Graph sampling based inductive learning method,
H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V . Prasanna, “Graph- saint: Graph sampling based inductive learning method,”arXiv preprint arXiv:1907.04931, 2020
1907 arXiv
Reviewed August 5, 2026 · model on record in the stance chip above.
Discussion (0). Sign in to comment.