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REVIEW 4 major objections 6 minor 17 references

Optimization of C-band quantum traffic coexisting with O-band classical traffic: preliminary results

T0 review · 4 major / 6 minor · reviewed 2026-07-30 · grok-4.5

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Pith's one-line read A source-independent model of Raman noise picks the quietest C-band channels for quantum light sharing fiber with O-band classical traffic.

desk verdict Useful spool measurements and a practical C-band minimum near 1535 nm for O-band classical; the “source-independent predictive model” is mostly a private polynomial fit plus standard L_eff. read the letter →

arxiv 2607.26905 v1 pith:PBMPXYBA submitted 2026-07-29 quant-ph

Laura d'Avossa , Elena Montella , Marco Grillo , Angela Sara Cacciapuoti , Marcello Caleffi This is my paper · ORCID
classification quant-ph
keywords quantum-classicalcoexistencespontaneousRamanscatteringC-bandquantumchannelsO-bandclassicaltrafficsingle-modefiberSFPtransceiverDWDMchannelallocationurbannetworks
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Quantum networks will have to share existing fiber with ordinary classical traffic. When classical light sits in the O-band (around 1310 nm) and quantum light sits in the C-band, spontaneous Raman scattering turns some classical photons into broadband noise that lands on the quantum channels. This paper measures that noise with commercial SFP transceivers and ordinary single-mode fiber spools up to 5 km, then fits a compact formula that predicts the noise from launch power, wavelength and length. The formula’s spectral shape is essentially the same for three different sources, so the quietest C-band channels (near 1535 nm) can be chosen without re-measuring every transmitter. The practical payoff is a simple engineering rule for placing entanglement or QKD channels on urban fiber that already carries classical traffic.

What carries the argument

The compact count model C_SpRS(S_src, λ, L) with a source-independent fourth-degree polynomial A(λ) for the Raman spectral profile at fixed 1310 nm pump; it separates power scaling, wavelength shape and fiber-length attenuation so optimal C-band channels can be read off without new spectral scans.

What would settle it

Measure SpRS counts versus C-band wavelength on a different fiber type or a longer deployed urban loop with a fourth commercial O-band source; if the same fourth-degree A(λ) no longer predicts the location or depth of the minimum within the reported relative-error bounds, the claimed generality fails.

Watch

Extended reading notes

Core claim

Spontaneous Raman scattering from O-band classical light into the C-band is captured by the source-scaled expression C_SpRS(S_src, λ, L) = S_src A(λ) (e^{-α_c L} – e^{-α_o L})/(α_o – α_c), where A(λ) is a single fourth-degree polynomial fixed once from experiment. The same A(λ) works across commercial SFPs and a narrow-linewidth laser, revealing a clear noise minimum near 1535 nm (ITU channel 44) that can be used to allocate quantum channels.

Load-bearing premise

The single polynomial shape of the Raman spectrum measured on laboratory spools, together with two fixed attenuation numbers, stays accurate enough for other fibers and real urban links without being re-fit.

Editorial extensions

If this is right

  • Quantum-network operators can pre-select C-band DWDM channels near 1535 nm to minimize Raman noise from co-propagating 1310 nm classical traffic.
  • Noise budgets for entanglement distribution or QKD on shared urban fiber can be estimated from launch power and length alone, without repeated spectral characterization of every transmitter.
  • The same model supplies a quantitative figure of merit for deciding when O-band classical / C-band quantum co-propagation is tolerable versus when spatial or temporal isolation is required.
  • Filter-cascade design rules (number of WDMs) can be set by the same measurements that produced the model.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the polynomial A(λ) proves stable across manufacturers, operators could publish a single look-up table of preferred quantum channels for any O-band classical plant.
  • The observed minimum may shift under temperature or aging of the fiber; a short field re-calibration protocol would turn the model into a live network-management tool.
  • Extending the same fitting procedure to multi-wavelength O-band loads would immediately give noise maps for fully loaded classical links.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The manuscript reports experimental measurements of C-band spontaneous Raman scattering (SpRS) generated by co-propagating O-band classical traffic at 1310 nm in standard single-mode fiber, using commercial SFP transceivers as well as a narrow-linewidth laser. Cascaded WDMs and a tunable narrowband filter isolate SpRS counts across ITU channels 15–63 for fiber lengths of 100 m, 500 m and 5 km. From these data the authors propose the compact model C_SpRS(S_src, λ, L) = S_src A(λ) L_eff (Eq. 3), where L_eff is the usual effective length and A(λ) is a single fourth-degree polynomial fitted to the combined spool measurements. They report a spectral minimum near ~1535 nm (channel 44), relative errors in Table I, and argue that the model is source-independent and therefore usable to allocate optimal C-band quantum channels in the presence of O-band classical traffic.

Significance. Quantum–classical coexistence on deployed single-core fiber is a genuine deployment bottleneck; measurements that employ commercial SFPs rather than laboratory lasers, and that scan the full C-band DWDM grid, are practically valuable. The consistent observation of a count minimum near 1535 nm across three sources and several lengths is a concrete, actionable finding for the authors’ testbed and similar urban links. If the claimed source-agnostic, length-and-power-scalable A(λ) were fully specified and shown to transfer, the work would supply a useful engineering tool. At present the contribution is best read as a careful empirical characterization plus a descriptive fit, not yet as a parameter-robust predictive model for diverse systems.

major comments (4)
  1. [§V, Eq. (3)] §V and Eq. (3): A(λ) is defined as a fourth-degree polynomial “derived from the experimental measurements,” yet no coefficients, covariance, residual spectrum, or fitting procedure (weights, wavelength grid, joint vs. sequential fit) are supplied. Without these numbers the model cannot be reproduced, independently checked, or applied by a reader to a new link. This is load-bearing for every claim of a “predictive,” “compact,” and “parameter-robust” tool in the abstract and §VI.
  2. [§V, Table I] §V, Figs. 4–7 and Table I: Validation largely re-compares the same (or power-normalized) spool datasets used to build A(λ). Relative errors already reach 14–32 % on 100 m spools and remain several percent at 5 km (e.g., 13.3 % commercial source, 5 km). The manuscript acknowledges that short-fiber Raman is weak relative to background, but still presents those points as supporting “robustness.” A held-out fiber length, a different fiber type, or an independent pump wavelength would be needed to substantiate transferability; none is provided.
  3. [§VI, Abstract] §VI and abstract: The language “parameter-robust description o applicable to diverse fiber-based systems” and “independent of the specific optical source” overreaches the evidence. All data use a single pump wavelength (1310 nm), standard SMF spools (plus a brief mention of a 7 km urban loop without quantitative comparison), and three sources whose differences are absorbed into a free scale S_src. Fixed α_o = 0.35 dB/km and α_c = 0.2 dB/km are stated without measured values or sensitivity analysis. The generality claim should be narrowed to the measured regime, or additional cross-checks added.
  4. [§VI, Fig. 8] Fig. 8 / §VI: The comparison with “the theoretical model” is central to the claim that the empirical fit captures structure missed by established Raman theory, yet the theoretical curve’s precise inputs (Raman gain spectrum used, filter bandwidth, absolute scaling) are not stated. Without that specification the reader cannot judge whether the missing minimum is a genuine fiber/source effect or a mismatch in normalization or spectral resolution.
minor comments (6)
  1. [§II, Eq. (1)] Eq. (1) writes β(λ_o λ_c) without a comma or semicolon; later text uses β(λ_o, λ_c). Standardize notation.
  2. [§V] α_o and α_c are given in dB/km in the text of §V but enter Eq. (2)–(3) as neperian coefficients; state the unit conversion explicitly.
  3. [§IV, Fig. 3] Fig. 3 caption and body refer interchangeably to “WDM” and “DWDM” for the C-band cascade; clarify which devices are used where.
  4. [§VI] The 7 km urban-loop validation is asserted in §VI with a citation to an invited workshop paper but no counts, errors, or overlay figure appear here; either add a brief quantitative panel or soften the claim.
  5. Typos / style: “WMD” for WDM (p. 4); “e −αoL” spacing in Eq. (2); “National Quantum Internet.it” formatting; duplicate reference [15]/[18].
  6. [§III] State the SNSPD detection efficiency and DCR at the actual scanned wavelengths (not only 1550 nm) or confirm that the relative spectral shape is unaffected.

Circularity Check

2 steps flagged · score 5.0 of 10

A(λ) is a fourth-degree polynomial fitted to the same SpRS-vs-λ counts the model is then said to predict; spectral shape and channel minimum are therefore descriptive of the fit, not an independent first-principles result.

  1. fitted input called prediction [§V, Eq. (3); Figs. 4–5; Table I]
    "From the measured SpRS counts C_SpRS it is possible to derive the following model: C_SpRS(S_src, λ, L) = S_src A(λ) (e^{-α_c L} - e^{-α_o L})/(α_o - α_c) ... A(λ) represents the wavelength-dependent Raman spectral profile ... modeled through a polynomial function derived from the experimental measurements obtained with both sources. ... the fourth-degree polynomial provides the best approximation of the experimental trend"

    A(λ) is obtained by polynomial regression on the measured C_SpRS(λ) curves (laser + commercial SFP, 500 m and 5 km). Eq. (3) then multiplies that fitted A(λ) by a source scale and the known L_eff factor and is plotted back against the same class of SpRS-vs-λ data as a “theoretical model.” For every wavelength point used in the fit, the spectral factor of the “prediction” equals the fit by construction; relative errors in Table I for those sources/lengths therefore partly restate training residual, not an out-of-sample spectral law.

  2. fitted input called prediction [Abstract; §V–§VI (channel selection claim)]
    "The model can therefore be used for the identification of optimal C-band channels for quantum signal allocation, namely those least affected by SpRS noise generated by co-propagating O-band classical traffic. ... a clear minimum is observed around 1535 nm (channel 44), suggesting that this channel is the most suitable"

    The abstract and discussion present the compact model as what enables optimal-channel identification. The only wavelength dependence in Eq. (3) is the empirically fitted A(λ); the reported minimum is the minimum of that fit (and of the same measured curves). No independent Raman coefficient or external spectral law is used. Channel ranking is thus the ranking of the fitted polynomial (or of the training spectra), not a prediction derived from something other than the SpRS profile being ranked. (Raw §IV counts already show the dip, so the scientific observation is real; the circularity is in packaging the fit as the predictive selector.)

full rationale

The paper’s load-bearing object is Eq. (3): C_SpRS(S_src, λ, L) = S_src A(λ) L_eff, with L_eff the standard effective-length factor and fixed α_o, α_c. A(λ) is explicitly “modeled through a polynomial function derived from the experimental measurements obtained with both sources,” and degree 4 is chosen because it best matches those curves (Fig. 4). Agreement of that same functional form with the fitting sources/lengths (Fig. 5, parts of Table I) is therefore forced by construction for the spectral factor. That is a clear fitted-input-called-prediction step for the wavelength dependence that drives “optimal channel” language. The circularity is only partial: (i) the minimum near 1535 nm / ch. 44 is already visible in the raw WDM-cascade counts of §IV before any polynomial is introduced; (ii) holding A(λ) fixed and checking a third manufacturer (Huawei) and a 100 m spool is a genuine transfer test, not a tautology; (iii) L_eff and S_src are not fitted spectral shapes. There is no load-bearing self-citation uniqueness chain. Score 5 reflects one central “prediction” that reduces to its fit, while the source- and length-transfer claims retain independent empirical content.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The load-bearing content is experimental SpRS counts plus an empirical factorization into a fitted spectral shape A(λ), a source scale S_src, and a standard effective-length term with hand-set attenuations. No new physical entity is postulated. Generality claims rest on assuming the fitted A(λ) and fixed α values travel beyond the measured modules and spools.

free parameters (3)
  • A(λ) fourth-degree polynomial coefficients = coefficients not reported; degree 4 selected
    Wavelength-dependent Raman profile at fixed 1310 nm pump is entirely obtained by polynomial fit to the measured SpRS counts (§V); degree chosen by visual/fit comparison in Fig. 4.
  • S_src (per-source scale factor) = source-dependent; numeric values not tabulated
    Absorbs different launch powers and source-dependent amplitude so curves can be overlaid; adjusted per optical source (§V, Fig. 5–7).
  • α_o and α_c attenuation coefficients = α_o = 0.35 dB/km, α_c = 0.2 dB/km
    Fixed to ‘physically realistic’ values rather than measured on the same spools for each wavelength (§V).
assumptions (4)
  • domain assumption SpRS power takes the standard form P_Raman(λ_o,λ_c) = β(λ_o,λ_c) P_o Δλ L_eff with L_eff = (e^{-α_c L} - e^{-α_o L})/(α_o - α_c).
    Adopted in §II from prior fiber literature [15]–[18] as the backbone of Eq. (3).
  • ad hoc to paper A single polynomial A(λ) at fixed 1310 nm pump captures the Raman spectral profile for commercial SFPs and narrow-linewidth lasers alike after power scaling.
    Core modeling choice in §V; used to claim source-agnostic robustness.
  • domain assumption Commercial SFP modules plus SMF spools up to ~5–7 km represent deployed urban coexistence conditions for channel selection.
    Stated motivation in abstract/§I and discussion of urban loop [6] in §VI.
  • domain assumption Cascaded WDM/DWDM filtering sufficiently isolates true SpRS from out-of-band source leakage and detector background for the reported minima to be physical.
    §IV argues at least one (preferably two) O-band WDMs and multiple C-band stages are required; residual leakage is a measurement risk.

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Cite this review

Pith. "Pith review of Optimization of C-band quantum traffic coexisting with O-band classical traffic: preliminary results." pith.science (2026). https://pith.science/paper/PBMPXYBA

@misc{pith2026260726905,
  author       = {Pith},
  title        = {Pith review of: Optimization of C-band quantum traffic coexisting with O-band classical traffic: preliminary results},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PBMPXYBA}},
  note         = {Machine review of arXiv:2607.26905}
}
read the original abstract

The coexistence of quantum and classical signals in the same optical fiber is a critical challenge for the deployment of quantum networks. Indeed, selecting an optimal channel for quantum signal transmission is crucial to minimize noise arising from co-propagating classical signals. This work experimentally investigates spontaneous Raman scattering (SpRS), a major source of noise in signals transmitted along the same fiber. Unlike most previous studies relying on narrow-linewidth laboratory lasers or architectures based on spatial or temporal multiplexing of quantum and classical signals, we employ commercial SFP optical transceivers and standard single-core single-mode fiber for the transmission of quantum and classical signals in the same fiber, reflecting conditions typical of deployed urban fiber infrastructures. Building on these measurements, we derive a compact and predictive model that captures the Raman scattering profile, enabling accurate estimation of SpRS noise as a function of source power, wavelength, and fiber length. A key outcome of this work is that the proposed model is independent of the specific optical source used, demonstrating its generality and robustness. The model can therefore be used for the identification of optimal C-band channels for quantum signal allocation, namely those least affected by SpRS noise generated by co-propagating O-band classical traffic. These results pave the way for a parameter-robust description of Raman scattering applicable to diverse fiber-based systems.

Figures

Figures reproduced from arXiv: 2607.26905 by the authors.

Figure 1
Figure 1. Experimental setup for the measurement C-band SpRS [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Transmission profile of the tunable narrowband filter. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. SpRS counts CSpRS generated in the C-band from a classical O-band source at 1310 nm. (a) 500 m (b) 5000 m [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: SpRS counts CSpRS for different fiber lengths, comparing experimental measurements with theoretical model of Eq. 3. The model is evaluated using polynomial fit of increasing order for A(λ). undergo significant variations during measurement acquisition. The O-band signa…
Figure 5
Figure 5. Figure 5: Comparison between measured CSpRS, acquired using two optical sources and fiber spool lengths of 500 m and 5 km, with the corresponding theoretical model. The experimental data are normalized to account for the different output powers of the sources. The theoretical cu…
Figure 6
Figure 6. Figure 6: SpRS counts CSpRS for different fiber lengths: comparison between experimental data and derived fits for the third source (Huawei SFP) and the previously analyzed reference classical sources [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: Experimental data and derived fits for all three sources [PITH_FULL_IMAGE:figures/full_fig_p005_7.png]
Figure 8
Figure 8. Figure 8: Comparison of SpRS counts: theoretical model versus [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]

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