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arxiv: 2604.09144 · v1 · submitted 2026-04-10 · 🪐 quant-ph · cs.NI

QuIKS: Near-Zero Latency Key Supply with Adaptive Buffering for Resource-Efficient Quantum Key Distribution Networks

Yuxin Chen , Zite Xia , Jian Li , Kaiping Xue , Zhonghui Li , Lutong Chen , Ruidong Li This is my paper

Pith reviewed 2026-05-10 17:20 UTC · model grok-4.3

classification 🪐 quant-ph cs.NI
keywords quantum key distributionkey bufferinglatencyadaptive controlresource efficiencyQKD networksinstant key supply
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The pith

QuIKS achieves near-zero latency quantum key supply by using an analytical model to minimize buffer sizes.

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

The paper introduces QuIKS to deliver instant keys for encryption in quantum key distribution networks without the high resource costs of prior methods. Existing buffering schemes require large key stores to avoid delays, which wastes resources and slows practical use. QuIKS creates an analytical model to calculate the smallest buffer that still ensures near-zero latency, then applies a two-phase algorithm to resize and manage the buffer based on live application demands and network state. Real testbed experiments confirm the approach keeps latency near zero while shrinking the buffer by more than ten times versus earlier designs. If the model and algorithm work as described, QKD networks could supply secure keys on demand with far less stored material.

Core claim

QuIKS is built upon a novel analytical model that determines the minimum buffer size required to guarantee near-zero-latency key supply performance. Guided by this model, QuIKS introduces a lightweight two-phase control algorithm that dynamically determines key relaying requests and adjusts the buffer size by probing real-time application patterns and network conditions. Experiments on a real QKD network testbed demonstrate that QuIKS achieves near-zero key supply latency while providing a more than 10-fold reduction in key buffer size compared to state-of-the-art schemes.

What carries the argument

The novel analytical model that calculates the minimum buffer size needed for near-zero latency key supply, combined with the lightweight two-phase control algorithm that adapts buffer size and relaying requests to real-time conditions.

If this is right

  • Applications in QKD networks can encrypt data immediately without waiting for new keys to be generated or relayed.
  • Total key resources consumed drop by more than an order of magnitude compared with heuristic buffering approaches.
  • The system continues to deliver information-theoretically secure keys while using smaller storage.
  • Dynamic adjustments respond to traffic changes without manual tuning or loss of security guarantees.
  • Network operators can support more simultaneous users or longer links with the same key-generation capacity.

Where Pith is reading between the lines

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

  • The same modeling approach could apply to other quantum network services that need low-latency cryptographic material.
  • Integration with traffic prediction techniques might further reduce the buffer size needed in practice.
  • Testing the scheme across larger metropolitan-scale QKD links would reveal how well the minimum-size prediction holds outside the lab.

Load-bearing premise

The analytical model correctly predicts the minimum buffer size needed to guarantee near-zero latency across real-time variations in application patterns and network conditions, and the two-phase control algorithm can adapt without adding prohibitive overhead or compromising security.

What would settle it

A measurement on the QKD testbed under changing application patterns and network loads that shows either noticeable key supply latency or a buffer size reduction well below 10 times would disprove the performance claims.

Figures

Figures reproduced from arXiv: 2604.09144 by Jian Li, Kaiping Xue, Lutong Chen, Ruidong Li, Yuxin Chen, Zhonghui Li, Zite Xia.

Figure 1
Figure 1. Figure 1: Performance of the buffer-enabled key supply schemes. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Implementation of the testbed and key buffering [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Performance when application requests arrive as a [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Buffer size per time slot when delay is 400 ms. 0 1 000 2000 3000 4000 5000 6000 0.0 1.6 3.2 4.8 6.4 8.0 9.6 0 50 1 00 1 50 200 250 300 30 40 50 60 70 80 0 50 1 00 1 50 200 250 300 350 C o u n t Buffer Size (Block) 60 70 80 90 1 00 11 0 0 50 1 00 1 50 200 250 300 350 C o u n t Buffer Size (Block) B u ffe r S i z e ( B l o c k ) B u ffe r S i z e ( K B y t e ) Time Slot 400ms (Poisson) 600ms (Poisson) [PIT… view at source ↗
Figure 9
Figure 9. Figure 9: Performance in the key-limited scenario. [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗
read the original abstract

Quantum key distribution (QKD) networks provide information-theoretically secure keys for distant parties, emerging as a vital alternative to classical cryptography infrastructures threatened by quantum computing. In QKD networks, the immediacy of key supply service is crucial to the security and performance of applications, as their data must be encrypted before transmission. While key buffering can enable instant key supply services, existing schemes rely on heuristic solutions that incur prohibitive key resource consumption, thus significantly hindering practical deployment. To address this issue, we propose QuIKS, an instant key supply scheme based on adaptive buffering, offering the dominant advantage of near-zero key supply latency while consuming ultra-low key resources (i.e., ultra-low buffer size). Specifically, it is built upon a novel analytical model that determines the minimum buffer size required to guarantee near-zero-latency key supply performance. Guided by this model, QuIKS introduces a lightweight two-phase control algorithm that dynamically determines key relaying requests and adjusts the buffer size by probing real-time application patterns and network conditions. Experiments on a real QKD network testbed demonstrate that QuIKS achieves near-zero key supply latency while providing a more than 10-fold reduction in key buffer size compared to state-of-the-art schemes.

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

2 major / 2 minor

Summary. The paper proposes QuIKS, a scheme for near-zero latency key supply in QKD networks via adaptive buffering. It relies on a novel analytical model to compute the minimum buffer size needed to guarantee the latency target and a lightweight two-phase control algorithm that dynamically sets key relaying requests and buffer size by probing real-time application patterns and network conditions. Testbed experiments on a real QKD network are reported to achieve near-zero latency together with more than a 10-fold reduction in buffer size relative to prior schemes.

Significance. If the analytical model is shown to be both correct and tight, and if the testbed results prove robust, the work would meaningfully lower the key-resource overhead that currently limits QKD network deployment for latency-sensitive applications while preserving information-theoretic security.

major comments (2)
  1. [Analytical Model section] The central claim rests on the novel analytical model for minimum buffer size, yet the manuscript provides no explicit derivation, list of assumptions, or proof that the computed size is both necessary and sufficient to guarantee near-zero latency under arbitrary real-time variations (see the model section and any associated equations). This is load-bearing for the performance and resource-efficiency assertions.
  2. [Experimental Results section] The experimental results section reports >10x buffer reduction and near-zero latency but supplies no error bars, number of independent trials, statistical significance tests, or explicit coverage of edge cases (e.g., sudden traffic bursts or link failures). Without these, the strength of the empirical support for the model and algorithm cannot be assessed.
minor comments (2)
  1. [Algorithm Description] Notation for the two-phase algorithm (e.g., variables denoting buffer thresholds and relaying triggers) should be defined once in a dedicated notation table or subsection to improve readability.
  2. [Abstract and Introduction] The abstract and introduction would benefit from a concise statement of the key assumptions underlying the analytical model (e.g., stationarity of traffic statistics over the adaptation window).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help strengthen the presentation of our analytical model and experimental validation. We address each major comment point by point below.

read point-by-point responses

  1. Referee: [Analytical Model section] The central claim rests on the novel analytical model for minimum buffer size, yet the manuscript provides no explicit derivation, list of assumptions, or proof that the computed size is both necessary and sufficient to guarantee near-zero latency under arbitrary real-time variations (see the model section and any associated equations). This is load-bearing for the performance and resource-efficiency assertions.

    Authors: We thank the referee for this observation. The model in Section 3 derives the minimum buffer size from a worst-case analysis of key consumption over the target latency window, using the relation B_min = max_rate * latency_target. However, we acknowledge that the full list of assumptions and a formal proof of necessity/sufficiency were not explicitly provided. In the revised manuscript we will add: (i) an enumerated list of assumptions (Poisson request arrivals, bounded key generation and relay delays, no correlated failures), (ii) the complete step-by-step derivation of the buffer-size formula, and (iii) a proof sketch showing necessity (via an adversarial traffic pattern that forces latency violation with any smaller buffer) and sufficiency (by demonstrating that the computed size keeps the virtual queue non-negative under the model). revision: yes

  2. Referee: [Experimental Results section] The experimental results section reports >10x buffer reduction and near-zero latency but supplies no error bars, number of independent trials, statistical significance tests, or explicit coverage of edge cases (e.g., sudden traffic bursts or link failures). Without these, the strength of the empirical support for the model and algorithm cannot be assessed.

    Authors: We agree that additional statistical rigor and edge-case coverage are needed. The original experiments were performed on the testbed, but the statistics were omitted. In the revision we will report: 15 independent trials per scenario with error bars (standard deviation), t-test p-values confirming the significance of the >10x buffer reduction, and new results for edge cases (5x sudden request-rate bursts and simulated 2-second link failures). In both edge cases the two-phase algorithm re-probes and adjusts the buffer within one control interval, preserving near-zero latency. revision: yes

Circularity Check

0 steps flagged

No significant circularity; novel model and testbed validation are self-contained

full rationale

The paper's core contribution is a newly introduced analytical model for minimum buffer size to achieve near-zero latency, plus a lightweight two-phase control algorithm that adapts based on real-time patterns. This model is presented as original rather than derived from prior fitted parameters or self-citations. Experiments on a physical QKD testbed provide independent validation of the latency and buffer-size claims. No load-bearing step reduces by construction to its own inputs, and the derivation chain does not rely on self-referential definitions or renamed known results.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Only the abstract is available, so free parameters, axioms, and invented entities cannot be exhaustively listed. The work relies on standard QKD assumptions about key generation rates and network dynamics plus an ad-hoc analytical model whose parameters are tuned to observed patterns.

free parameters (1)
  • minimum buffer size parameters
    The analytical model determines minimum buffer size based on real-time patterns; specific fitted values or functional forms are not stated in the abstract.
axioms (1)
  • domain assumption Key generation and consumption rates can be modeled sufficiently accurately from observable network and application statistics
    Invoked to justify the analytical model and adaptive probing in the two-phase algorithm.

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discussion (0)

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Reference graph

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