BARFI-Q: Quantum-Enhanced Block Attention Residual Fusion Framework for Multivariate Time-Series Forecasting in Atom Interferometry

• URL: http://arxiv.org/abs/2605.05394v1
• Date: Wed, 06 May 2026 19:26:56 GMT
• Title: BARFI-Q: Quantum-Enhanced Block Attention Residual Fusion Framework for Multivariate Time-Series Forecasting in Atom Interferometry
• Abstract summary: This paper proposes a Quantum-Enhanced Block Attention Residual Fusion framework for time-series forecasting in atom interferometry.<n>BarFI-Q integrates patch-based embedding, dual-branch temporal modeling, hierarchical fusion, adaptive block-attention residual aggregation, and a quantum feature-mapping module.<n>Results indicate that multiscale temporal learning, hierarchical fusion, adaptive residual routing, and quantum-enhanced latent transformation provide an effective framework for atom-interferometric time-series forecasting.
• Score: 3.2350449526046567
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Atom interferometry generates heterogeneous multivariate temporal streams governed by phase evolution, fringe dynamics, control variables, and auxiliary sensing measurements. Accurate forecasting of these signals is important for predictive monitoring, phase correction, and intelligent quantum sensing, but it requires effective modeling of long-range temporal dependencies and interactions among multiple sensing sources. This paper proposes BARFI-Q, a Quantum-Enhanced Block Attention Residual Fusion framework for multivariate time-series forecasting in atom interferometry. BARFI-Q integrates patch-based embedding, dual-branch temporal modeling, hierarchical fusion, adaptive block-attention residual aggregation, and a quantum feature-mapping module. Unlike conventional Transformer-based forecasting models with fixed additive residual paths, BARFI-Q adaptively reuses cross-depth information and enhances the fused latent representation through quantum feature mapping. To respect phase periodicity, the forecasting target is represented in circular space using sine and cosine components. Experiments show that BARFI-Q consistently outperforms strong baseline models across repeated runs and different historical window sizes. Fusion ablation results further confirm the benefit of jointly modeling channel-wise and spatial feature interactions. These results indicate that multiscale temporal learning, hierarchical fusion, adaptive residual routing, and quantum-enhanced latent transformation provide an effective framework for atom-interferometric time-series forecasting.

Related papers

• A Quantum-Classical Hybrid Framework for Multivariate Time-Series Forecasting Complexity-Fidelity Trade-offs and Limitations [4.77829808535938]
  This paper introduces two model variants Quantum Reservoir Forecaster (QRC-F) and Variational Quantum Forecaster (VQF-F)<n>The proposed framework investigates the complexity-fidelity trade-off of quantum forecasting under near-term NISQ hardware constraints.<n>Results establish a practical quantum-native forecasting framework with strong potential for deployment on near-term NISQ devices.
  arXiv  Detail & Related papers  (2026-07-17T10:30:12Z)
• PQFA: Parallel Quantum Feature Augmentation of Fused Representations for Multimodal Classification [2.920575300184085]
  We propose Parallel Quantum Feature Augmentation (PQFA), a hybrid quantum-classical framework that applies multiple shallow variational quantum circuits to fused multimodal features.<n>We evaluate PQFA on MM-IMDb and N24-News through controlled comparisons using the same encoders, fusion backbone, data splits, projection dimension, and augmentation parameters.
  arXiv  Detail & Related papers  (2026-07-15T05:53:58Z)
• PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting [62.22445401483844]
  Existing methods are severely constrained by the bottleneck of compounding errors.<n>We propose a universal framework called -Cast-Corrector (Plug-and-Play Corrector)<n>Our method significantly enhances the long-term stability of coupled forecasting systems.
  arXiv  Detail & Related papers  (2026-05-09T13:12:33Z)
• PAMNet: Cycle-aware Phase-Amplitude Modulation Network for Multivariate Time Series Forecasting [42.86661738011748]
  We propose a Cycle-aware Phase-Amplitude Modulation Network (PAMNet) that explicitly decomposes periodic patterns into complementary phase and amplitude components.<n>Our method achieves state-of-the-art performance through its novel phase-amplitude decoupling mechanism.
  arXiv  Detail & Related papers  (2026-05-01T06:59:59Z)
• UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration [24.102050375727263]
  UniMamba is a unified spatial-temporal forecasting framework.<n>It integrates efficient state-space dynamics with attention-based dependency learning.<n>It consistently outperforms state-of-the-art forecasting models in both forecasting accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2026-03-06T05:00:28Z)
• DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters [50.43534351968113]
  Existing generative time series models do not address the multi-dimensional properties of time series data well.<n>Inspired by Multimodal Diffusion Transformers that integrate textual guidance into video generation, we propose Diffusion Transformers for Time Series (DiTS)
  arXiv  Detail & Related papers  (2026-02-06T10:48:13Z)
• UniDiff: A Unified Diffusion Framework for Multimodal Time Series Forecasting [90.47915032778366]
  We propose UniDiff, a unified diffusion framework for multimodal time series forecasting.<n>At its core lies a unified and parallel fusion module, where a single cross-attention mechanism integrates structural information from timestamps and semantic context from texts.<n>Experiments on real-world benchmark datasets across eight domains demonstrate that the proposed UniDiff model achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-12-08T05:36:14Z)
• Morphing Through Time: Diffusion-Based Bridging of Temporal Gaps for Robust Alignment in Change Detection [51.56484100374058]
  We introduce a modular pipeline that improves spatial and temporal robustness without altering existing change detection networks.<n>A diffusion module synthesizes intermediate morphing frames that bridge large appearance gaps, enabling RoMa to estimate stepwise correspondences.<n>Experiments on LEVIR-CD, WHU-CD, and DSIFN-CD show consistent gains in both registration accuracy and downstream change detection.
  arXiv  Detail & Related papers  (2025-11-11T08:40:28Z)
• Multi-modal Adaptive Estimation for Temporal Respiratory Disease Outbreak [8.861941883057098]
  MAESTRO is a novel, unified framework that integrates spectro-temporal modeling with multi-modal data fusion.<n> Evaluated on over 11 years of Hong Kong data, MAESTRO achieves a superior model fit with an R-square of 0.956.<n>The modular and reproducible pipeline is made publicly available to facilitate deployment and extension to other regions and pathogens.
  arXiv  Detail & Related papers  (2025-09-10T13:27:40Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [42.60778405812048]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• Integrating Quantum-Classical Attention in Patch Transformers for Enhanced Time Series Forecasting [4.580983642743026]
  QCAAPatchTF is a quantum attention network integrated with an advanced patch-based transformer.<n> leveraging quantum superpositions, entanglement, and variational quantum eigensolver principles.<n>QCAAPatchTF achieves state-of-the-art performance in both long-term and short-term forecasting, classification, and anomaly detection tasks.
  arXiv  Detail & Related papers  (2025-03-31T17:23:36Z)
• MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecasting [51.94256702463408]
  Time series predictability is derived from periodic characteristics at different frequencies.<n>We propose a novel time series forecasting method based on multi-frequency reference series correlation analysis.<n> Experiments on major open and synthetic datasets show state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-11T11:40:14Z)
• Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting [16.782154479264126]
  Predicting backbone-temporal traffic flow presents challenges due to complex interactions between temporal factors. Existing approaches address these dimensions in isolation, neglecting their critical interdependencies. In this paper, we introduce Sanonymous-Temporal Unitized Unitized Cell (ASTUC), a unified framework designed to capture both spatial and temporal dependencies.
  arXiv  Detail & Related papers  (2024-11-14T07:34:31Z)
• CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting [50.23240107430597]
  We design a special Transformer, i.e., Channel Aligned Robust Blend Transformer (CARD for short), that addresses key shortcomings of CI type Transformer in time series forecasting. First, CARD introduces a channel-aligned attention structure that allows it to capture both temporal correlations among signals. Second, in order to efficiently utilize the multi-scale knowledge, we design a token blend module to generate tokens with different resolutions. Third, we introduce a robust loss function for time series forecasting to alleviate the potential overfitting issue.
  arXiv  Detail & Related papers  (2023-05-20T05:16:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.