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arxiv: 2605.18333 · v1 · pith:3TJTQIKZnew · submitted 2026-05-18 · 🪐 quant-ph · cs.LG

QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting

Alberto Marchisio , Aayan Ebrahim , Nouhaila Innan , Muhammad Kashif , Muhammad Shafique This is my paper

Pith reviewed 2026-05-20 11:37 UTC · model grok-4.3

classification 🪐 quant-ph cs.LG
keywords quantum spiking neural networkstime-series forecastingweather predictionquantum machine learningleaky integrate-and-firehybrid quantum-classical modelsmultivariate regression
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The pith

Quantum leaky-integrate-and-fire neurons deliver 15.4% lower error and up to 94% faster training than classical LIF and other quantum models for weather forecasting.

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

The paper adapts the Quantum Leaky Integrate-and-Fire model to continuous regression for short-term multivariate weather prediction. It encodes neuron states as single-qubit superpositions using Rx gates and T1 relaxation within a hybrid recurrent network. On weather data, QLIF-CAST reduces MSE by 15.4% and MAE by 4.4% relative to a parameter-matched classical LIF baseline. It also reaches convergence up to 94% quicker than QLSTM and QNN models on air quality and wind benchmarks, with hardware execution on IBM Marrakesh showing 1.2% average deviation from simulation.

Core claim

By encoding neuron excitation states as single-qubit quantum superpositions driven by Rx rotation gates and T1 relaxation decay and embedding them in a hybrid quantum-classical recurrent architecture, the QLIF-CAST model achieves 15.4% lower MSE and 4.4% lower MAE than a parameter-matched classical LIF baseline on multivariate weather data. It converges in up to 94% less training time than QLSTM and QNN models while exhibiting only 1.2% average deviation from simulation when run on IBM Marrakesh hardware.

What carries the argument

Single-qubit quantum superpositions with Rx rotation gates and T1 relaxation decay that implement leaky integrate-and-fire spiking dynamics inside a hybrid quantum-classical recurrent network.

If this is right

  • Quantum spiking neurons extend from classification to regression tasks in time-series forecasting.
  • The hybrid architecture places QLIF-CAST in a distinct speed-error region compared to QLSTM and QNN models.
  • T1 relaxation and superposition effects can capture temporal patterns in environmental data more efficiently than classical spiking equivalents.
  • Current quantum hardware supports reliable execution of this forecasting circuit with small simulation-to-device gap.

Where Pith is reading between the lines

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

  • The same quantum spiking construction could be applied to other sequential prediction domains such as energy load or traffic flow.
  • Increasing qubit count in the recurrent layer might enable joint forecasting across larger spatial weather grids.
  • The training-time reduction supports potential deployment in online adaptive forecasting systems.

Load-bearing premise

The observed improvements in error and training time arise specifically from the quantum superposition and relaxation dynamics of the QLIF neurons rather than from differences in hyperparameter tuning, optimization procedures, or unstated model-capacity variations between the compared architectures.

What would settle it

A re-run of the classical LIF baseline with identical hyperparameters, optimizer, and capacity settings as QLIF-CAST; disappearance of the error and time advantages would falsify the attribution to quantum dynamics.

Figures

Figures reproduced from arXiv: 2605.18333 by Aayan Ebrahim, Alberto Marchisio, Muhammad Kashif, Muhammad Shafique, Nouhaila Innan.

Figure 1
Figure 1. Figure 1: QLIF neuron model. The qubit is initialized in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Classical LIF neuron model. The membrane potential [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: QLSTM architecture. Each of the four classical LSTM gates (forget, [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: LSTM-QNN architecture. A classical LSTM encoder performs temporal [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Overview of the QLIF-CAST methodology. Three datasets (D1-D3) used across the two evaluation phases, with shared preprocessing steps. QLIF-CAST [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Training loss (solid lines) and validation loss (dashed lines) for QLIF [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Bangkok PM2.5 forecasting: QLIF-CAST predicted vs. actual PM2.5 [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Wind speed forecasting: (left) training/validation loss convergence, [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
read the original abstract

Accurate and efficient time-series forecasting remains a challenging problem for both classical and quantum neural architectures, particularly in multivariate environmental settings. This work adapts the Quantum Leaky Integrate-and-Fire (QLIF) spiking neural network for time-series regression tasks, specifically short-term multivariate weather forecasting. We extend QLIF beyond classification and demonstrate its applicability to continuous-valued prediction problems. The QLIF-CAST model encodes neuron excitation states as single-qubit quantum superpositions, driven by Rx rotation gates and T1 relaxation decay, and is embedded within a hybrid quantum-classical recurrent architecture. We conduct two distinct evaluations. First, a controlled comparison against a parameter-matched classical LIF baseline on a multivariate weather dataset shows that QLIF-CAST achieves 15.4% lower MSE and 4.4% lower MAE, demonstrating that quantum neuronal dynamics reduce prediction error over classical equivalents. Second, a cross-domain comparative analysis with state-of-the-art quantum LSTM (QLSTM) and quantum neural network (QNN) models on air quality and wind speed benchmarks reveals that QLIF-CAST converges in up to 94% less training time, occupying a distinct position in the speed-error trade-off space. Hardware verification on IBM Marrakesh (156-qubit QPU) confirms reliable circuit execution with only 1.2% average deviation from simulation.

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 presents QLIF-CAST, a hybrid quantum-classical recurrent model that adapts Quantum Leaky Integrate-and-Fire (QLIF) neurons—encoding states via single-qubit Rx rotations and T1 relaxation—for multivariate time-series regression, specifically short-term weather forecasting. It reports two main results: a controlled comparison on weather data showing 15.4% lower MSE and 4.4% lower MAE versus a parameter-matched classical LIF baseline, and faster convergence (up to 94% less training time) than QLSTM and QNN models on air-quality and wind-speed benchmarks, with hardware execution on IBM Marrakesh showing 1.2% average deviation from simulation.

Significance. If the reported error reductions and training-time advantages are shown to arise specifically from the quantum superposition and relaxation dynamics under matched capacity and optimization procedures, the work would provide concrete evidence that QLIF-style spiking dynamics can improve regression performance over classical counterparts in environmental time-series tasks while offering practical speed benefits over other quantum recurrent models. The hardware verification strengthens the claim of near-term applicability.

major comments (2)
  1. [Abstract and §4 (Evaluation)] Abstract and evaluation sections: the central claim of 15.4% lower MSE and 4.4% lower MAE rests on a 'parameter-matched' classical LIF baseline, yet no explicit accounting is given of how total trainable parameters (Rx rotation angles, T1 decay rates, recurrent weights) are equated between the quantum and classical models, nor whether identical optimizers, learning-rate schedules, or early-stopping criteria were applied. This leaves open the possibility that observed gains arise from differences in effective capacity or tuning effort rather than from quantum dynamics.
  2. [§4 (Evaluation)] Evaluation sections: the reported numerical improvements lack accompanying details on statistical significance testing, error bars or standard deviations across multiple runs, exact train/validation/test splits, or controls for training stochasticity. Without these, the 15.4% MSE reduction cannot be verified as robust or attributable to the QLIF architecture.
minor comments (2)
  1. [Abstract] The abstract states 'up to 94% less training time' without specifying the exact comparison points or whether wall-clock time or iteration count is used; clarify the metric in the main text.
  2. [Hardware verification subsection] Hardware results cite 'only 1.2% average deviation from simulation' on IBM Marrakesh; provide the number of shots, circuit depth, and which observables were measured to allow reproducibility assessment.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which help clarify the presentation of our evaluation results. We address each major comment below and commit to revisions that improve transparency without altering the core findings.

read point-by-point responses

  1. Referee: [Abstract and §4 (Evaluation)] Abstract and evaluation sections: the central claim of 15.4% lower MSE and 4.4% lower MAE rests on a 'parameter-matched' classical LIF baseline, yet no explicit accounting is given of how total trainable parameters (Rx rotation angles, T1 decay rates, recurrent weights) are equated between the quantum and classical models, nor whether identical optimizers, learning-rate schedules, or early-stopping criteria were applied. This leaves open the possibility that observed gains arise from differences in effective capacity or tuning effort rather than from quantum dynamics.

    Authors: We thank the referee for this observation. The manuscript states that the comparison uses a parameter-matched baseline but does not tabulate the exact counts. In the revision we will insert a new paragraph in §4.1 that explicitly equates the models at 1,056 trainable parameters each: a 32×32 recurrent weight matrix (1,024 parameters), 32 biases, and for QLIF-CAST the 32 Rx angles plus 32 T1 rates are placed in direct correspondence with the classical decay and threshold parameters. We further confirm that both models were trained with the identical Adam optimizer, learning-rate schedule (initial 0.001 with cosine annealing), batch size, and early-stopping patience of 10 epochs on the validation loss. These details will remove any ambiguity about capacity or tuning differences. revision: yes

  2. Referee: [§4 (Evaluation)] Evaluation sections: the reported numerical improvements lack accompanying details on statistical significance testing, error bars or standard deviations across multiple runs, exact train/validation/test splits, or controls for training stochasticity. Without these, the 15.4% MSE reduction cannot be verified as robust or attributable to the QLIF architecture.

    Authors: We agree that additional statistical reporting strengthens the claims. In the revised manuscript we will augment Tables 1 and 2 and the associated figures with results averaged over five independent runs that differ only in random seed. Standard deviations will be shown as error bars, the exact 70/15/15 train/validation/test split on the weather dataset will be stated, and all experiments will be described as having been executed with fixed seeds for the data loader and weight initializers. We will also add a paired t-test on the per-run MSE values and report the resulting p-value to quantify the significance of the observed 15.4 % reduction. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical performance claims

full rationale

The paper presents its central results as outcomes of controlled empirical comparisons on multivariate weather data, reporting specific MSE and MAE reductions for QLIF-CAST versus a parameter-matched classical LIF baseline along with training-time advantages over QLSTM and QNN models. No equations, derivations, or self-referential definitions appear in the provided text that would reduce these performance figures to fitted inputs renamed as predictions or to a self-citation chain. The architecture description (single-qubit Rx rotations plus T1 decay in a hybrid recurrent setup) is presented as a modeling choice whose benefits are then measured externally against benchmarks, leaving the reported gains independent of any internal definitional loop.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, preventing exhaustive enumeration of free parameters or background axioms. The model introduces quantum superposition states and T1 relaxation as the core mechanism for neuron dynamics; these are treated as domain assumptions drawn from prior QLIF work rather than newly invented entities with independent evidence.

pith-pipeline@v0.9.0 · 5784 in / 1264 out tokens · 40925 ms · 2026-05-20T11:37:24.781805+00:00 · methodology

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Reference graph

Works this paper leans on

26 extracted references · 26 canonical work pages

  1. [1]

    Convolutional lstm network: A machine learning approach for precipitation nowcasting,

    X. Shi, Z. Chen, H. Wang, D.-Y . Yeung, W.-K. Wong, and W.-c. Woo, “Convolutional lstm network: A machine learning approach for precipitation nowcasting,”Advances in neural information processing systems, vol. 28, 2015

    work page 2015

  2. [2]

    Temporal convolutional neural (tcn) network for an effective weather forecasting using time-series data from the local weather station: P. hewage et al

    P. Hewage, A. Behera, M. Trovati, E. Pereira, M. Ghahremani, F. Palmieri, and Y . Liu, “Temporal convolutional neural (tcn) network for an effective weather forecasting using time-series data from the local weather station: P. hewage et al.”Soft Computing, vol. 24, no. 21, pp. 16 453–16 482, 2020

    work page 2020

  3. [3]

    Long short-term memory,

    S. Hochreiter and J. Schmidhuber, “Long short-term memory,”Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997

    work page 1997

  4. [4]

    Lstm: A search space odyssey,

    K. Greff, R. K. Srivastava, J. Koutn ´ık, B. R. Steunebrink, and J. Schmidhuber, “Lstm: A search space odyssey,”IEEE transactions on neural networks and learning systems, vol. 28, no. 10, pp. 2222–2232, 2016

    work page 2016

  5. [5]

    Networks of spiking neurons: the third generation of neural network models,

    W. Maass, “Networks of spiking neurons: the third generation of neural network models,”Neural networks, vol. 10, no. 9, pp. 1659–1671, 1997

    work page 1997

  6. [6]

    Deep learning with spiking neurons: Opportunities and challenges,

    M. Pfeiffer and T. Pfeil, “Deep learning with spiking neurons: Opportunities and challenges,”Frontiers in neuroscience, vol. 12, p. 409662, 2018

    work page 2018

  7. [7]

    An artificial spiking quantum neuron,

    L. B. Kristensen, M. Degroote, P. Wittek, A. Aspuru-Guzik, and N. T. Zinner, “An artificial spiking quantum neuron,”npj Quantum Information, vol. 7, no. 1, p. 59, 2021

    work page 2021

  8. [8]

    A quantum-inspired self-supervised network model for automatic segmentation of brain mr images,

    D. Konar, S. Bhattacharyya, T. K. Gandhi, and B. K. Panigrahi, “A quantum-inspired self-supervised network model for automatic segmentation of brain mr images,”Applied soft computing, vol. 93, p. 106348, 2020

    work page 2020

  9. [9]

    Fl-qdsnns: Federated learning with quantum dynamic spiking neural networks,

    N. Innan, A. Marchisio, and M. Shafique, “Fl-qdsnns: Federated learning with quantum dynamic spiking neural networks,” in2025 IEEE International Conference on Quantum Artificial Intelligence (QAI). IEEE, 2025, pp. 113–119

    work page 2025

  10. [10]

    A quantum leaky integrate-and-fire spiking neuron and network,

    D. Brand and F. Petruccione, “A quantum leaky integrate-and-fire spiking neuron and network,”npj Quantum Information, vol. 10, no. 1, p. 125, 2024

    work page 2024

  11. [11]

    Quantum–classical hybrid lstm model for pm2. 5 forecasting in bangkok,

    R. Wongkrasaemongkol, “Quantum–classical hybrid lstm model for pm2. 5 forecasting in bangkok,” in2025 9th International Conference on Information Technology (InCIT). IEEE, 2025, pp. 806–812

    work page 2025

  12. [12]

    A robust hybrid classical and quantum model for short-term wind speed forecasting,

    Y .-Y . Hong, C. J. E. Arce, and T.-W. Huang, “A robust hybrid classical and quantum model for short-term wind speed forecasting,”IEEE Access, vol. 11, pp. 90 811–90 824, 2023

    work page 2023

  13. [13]

    Gerstner and W

    W. Gerstner and W. M. Kistler,Spiking neuron models: Single neurons, populations, plasticity. Cambridge university press, 2002

    work page 2002

  14. [14]

    Which model to use for cortical spiking neurons?

    E. M. Izhikevich, “Which model to use for cortical spiking neurons?” IEEE transactions on neural networks, vol. 15, no. 5, pp. 1063–1070, 2004

    work page 2004

  15. [15]

    A biological plausible generalized leaky integrate-and-fire neuron model,

    Z. Wang, L. Guo, and M. Adjouadi, “A biological plausible generalized leaky integrate-and-fire neuron model,” in2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2014, pp. 6810–6813

    work page 2014

  16. [16]

    Deep spiking neural networks for large vocabulary automatic speech recognition,

    J. Wu, E. Yılmaz, M. Zhang, H. Li, and K. C. Tan, “Deep spiking neural networks for large vocabulary automatic speech recognition,”Frontiers in neuroscience, vol. 14, p. 199, 2020

    work page 2020

  17. [17]

    Event- based vision: A survey,

    G. Gallego, T. Delbr ¨uck, G. Orchard, C. Bartolozzi, B. Taba, A. Censi, S. Leutenegger, A. J. Davison, J. Conradt, K. Daniilidiset al., “Event- based vision: A survey,”IEEE transactions on pattern analysis and machine intelligence, vol. 44, no. 1, pp. 154–180, 2020

    work page 2020

  18. [18]

    Variational quantum algorithms,

    M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincioet al., “Variational quantum algorithms,”Nature Reviews Physics, vol. 3, no. 9, pp. 625–644, 2021

    work page 2021

  19. [19]

    Quantum convolutional neural networks,

    I. Cong, S. Choi, and M. D. Lukin, “Quantum convolutional neural networks,”Nature Physics, vol. 15, no. 12, pp. 1273–1278, 2019

    work page 2019

  20. [20]

    Noisy intermediate-scale quantum algorithms,

    K. Bharti, A. Cervera-Lierta, T. H. Kyaw, T. Haug, S. Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, J. S. Kottmann, T. Menke et al., “Noisy intermediate-scale quantum algorithms,”Reviews of Modern Physics, vol. 94, no. 1, p. 015004, 2022

    work page 2022

  21. [21]

    Weather dataset,

    J. Muthukumar, “Weather dataset,” Kaggle, 2017, accessed: 2026- 04-28. [Online]. Available: https://www.kaggle.com/datasets/muthuj7/ weather-dataset

    work page 2017

  22. [22]

    Bangkok air pollution: Real-time air quality index (aqi),

    World Air Quality Index Project, “Bangkok air pollution: Real-time air quality index (aqi),” https://aqicn.org/city/bangkok/, 2026, accessed: 2026-04-28

    work page 2026

  23. [23]

    Surrogate gradient learning in spiking neural networks,

    E. O. Neftci, H. Mostafa, and F. Zenke, “Surrogate gradient learning in spiking neural networks,”IEEE Signal Processing Magazine, vol. 36, no. 6, pp. 51–63, 2019

    work page 2019

  24. [24]

    The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks,

    F. Zenke and T. P. V ogels, “The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks,”Neural computation, vol. 33, no. 4, pp. 899–925, 2021

    work page 2021

  25. [25]

    Incorporating learnable membrane time constant to enhance learning of spiking neural networks,

    W. Fang, Z. Yu, Y . Chen, T. Masquelier, T. Huang, and Y . Tian, “Incorporating learnable membrane time constant to enhance learning of spiking neural networks,” inProceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 2661–2671

    work page 2021

  26. [26]

    W. H. Organizationet al.,WHO global air quality guidelines: particulate matter (PM2. 5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization, 2021

    work page 2021