arXiv AI

A Quantum-Classical Hybrid Framework for Multivariate Time-Series Forecasting Complexity-Fidelity Trade-offs and Limitations

arXiv:2607. 16358v1 Announce Type: cross Abstract: This paper presents a unified quantum-classical hybrid framework for multi-horizon time-series forecasting, introducing two model variants Quantum Reservoir Forecaster (QRC-F) and Variational Quantum Forecaster (VQF-F).

arXiv AI

Jun 16

Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning

arXiv:2605. 06734v2 Announce Type: replace-cross Abstract: Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states.

By Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang, Chen-Yu Liu, En-Jui Kuo, Yun-Yuan Wang, Prayag Tiwari, Andrea Ceschini, Chi-Sheng Chen, Yu-Chao Hsu, Chun-Hua Lin, Tai-Yue Li, Antonello Rosato, Massimo Panella, Simon See, Saif Al-Kuwari, Kuan-Cheng Chen, Nan-Yow Chen, Hsi-Sheng Goan

reinforcement-learning benchmarks

arXiv Machine Learning

Jul 23

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

arXiv:2605. 18333v2 Announce Type: replace-cross Abstract: Accurate and efficient time-series forecasting remains a challenging problem for both classical and quantum neural architectures, particularly in multivariate environmental settings.

By Alberto Marchisio, Aayan Ebrahim, Nouhaila Innan, Muhammad Kashif, Muhammad Shafique

benchmarks

arXiv Machine Learning

Sep 10

Sustained Performance and Energy Accounting for Nonlinear Forecasting Across Classical and Simulated Quantum Models

arXiv:2510.25183v2 Announce Type: replace-cross Abstract: Energy-efficient AI should be evaluated across the full application pipeline, not only by lowest error or shortest training time. We study th...

By Avyay Kodali, Priyanshi Singh, Pranay Pandey, Krishna Bhatia, Shalini Devendrababu, Srinjoy Ganguly

llms efficiency benchmarks

arXiv Machine Learning

Sep 18

Recursive Quantum Long Short-Term Memory for Stable Short-Horizon Temperature Forecasting

The paper introduces a recursive quantum long short-term memory (QLSTM) architecture and compares it to a standard QLSTM for one-step-ahead daily temperature forecasting. Using Toronto weather data and identical training settings, the recursive model consistently reaches near-optimal test loss earlier, achieves lower mean absolute error and root mean squared error, and shows a smaller generalization gap across input windows of 8, 16, and 32 days over 20 random seeds. These findings suggest that recursive quantum feature transformations can enhance stability and out-of-sample performance in compact hybrid quantum–classical temporal models.

By Mu-En Lee, Yen-Ku Liu, Samuel Yen-Chi Chen, Yun-Cheng Tsai

arXiv Machine Learning

Aug 18

Hybrid quantum recurrent neural network for remaining useful life prediction of turbofan engines

arXiv:2504. 20823v3 Announce Type: replace Abstract: Accurate remaining useful life (RUL) estimation underpins safe operation and cost-effective maintenance of aerospace propulsion systems.

By Olga Tsurkan, Aleksandra Konstantinova, Arsenii Senokosov, Asel Sagingalieva, Alexey Melnikov

benchmarks

arXiv Machine Learning

Jul 21

Multivariate Time Series Forecasting with Gate-Based Quantum Reservoir Computing on NISQ Hardware

arXiv:2510. 13634v2 Announce Type: replace Abstract: Quantum reservoir computing (QRC) offers a hardware-friendly approach to temporal learning, yet most studies target univariate signals and overlook near-term hardware constraints.

By Wissal Hamhoum, Soumaya Cherkaoui, Jean-Frederic Laprade, Ola Ahmad, Shengrui Wang

arXiv Machine Learning

Jul 31

Complementary Matrix-Gated QKAN Fast-Weight Programmers for Quantum Dynamics Forecasting

arXiv:2607. 27945v1 Announce Type: cross Abstract: Sequence models must decide what to write into memory and what to retain.

By Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang, Chen-Yu Liu, En-Jui Kuo, Yun-Yuan Wang, Tzung-Chi Huang, Prayag Tiwari, Chi-Sheng Chen, Chun-Hua Lin, Yu-Chao Hsu, Tai-Yue Li, Saif Al-Kuwari, Simon See, Kuan-Cheng Chen, Nan-Yow Chen, Hsi-Sheng Goan

benchmarks

arXiv Machine Learning

Jul 7

Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks

arXiv:2509. 14026v2 Announce Type: replace-cross Abstract: Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power of learnable activation functions.

By Jiun-Cheng Jiang, Morris Yu-Chao Huang, Tianlong Chen, Hsi-Sheng Goan

rag computer-vision safety

arXiv Machine Learning

Jun 8

LiQSS: Post-Transformer Linear Quantum-Inspired State-Space Tensor Networks for Real-Time 6G

arXiv:2601. 12375v3 Announce Type: replace-cross Abstract: Proactive and agentic control in Sixth-Generation (6G) Open Radio Access Networks (O-RAN) requires control-grade prediction under stringent Near-Real-Time (Near-RT) latency and computational constraints.

By Farhad Rezazadeh, Hatim Chergui, Amir Ashtari Gargari, Mehdi Bennis, Houbing Song, Lingjia Liu, Merouane Debbah

llms agents

arXiv AI

Jul 7

A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inference

arXiv:2603. 09789v3 Announce Type: replace-cross Abstract: Accurate financial volatility forecasting is crucial but challenged by the non-linear, highly correlated nature of market data.

By Yixiong Chen

efficiency

arXiv Machine Learning

Aug 4

QARIMA: A Quantum Approach To Classical Time Series Analysis

arXiv:2604. 08277v3 Announce Type: replace-cross Abstract: We present QARIMA, a quantum state-similarity-based reconstruction of the classical ARIMA modelling pipeline.

By Nishikanta Mohanty, Bikash K. Behera, Badshah Mukherjee, Pravat Dash, Giuseppe Sergioli, Roberto Giuntini

safety

arXiv AI

Jul 28

Multivariate Time Series Forecasting with Adaptive Non-Local Observables

arXiv:2607. 24399v1 Announce Type: cross Abstract: Multivariate time series forecasting (MTSF) predicts future values of multiple variables from historical data.

By Yu-Ting Lee, Huan-Hsin Tseng, Samuel Yen-Chi Chen

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