arXiv Machine Learning

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.

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 AI

Jul 3

Stable Self-Modulating Quantum Fast-Weight Programmers with Bounded Memory Gates

arXiv:2607. 02363v1 Announce Type: cross Abstract: Quantum Fast-Weight Programmers (QFWPs) store temporal information in dynamically programmed variational-circuit parameters rather than in nonlinear recurrent hidden states, offering a practical route to quantum sequence modeling.

By Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin, Yifeng Peng, Junghoon Justin Park, Huan-Hsin Tseng, Hsin-Yi Lin, Kuan-Cheng Chen, Chen-Yu Liu, Shinjae Yoo, Samuel Yen-Chi Chen

benchmarks

arXiv AI

Jun 29

Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting

arXiv:2606. 27821v1 Announce Type: cross Abstract: Traffic matrices (TMs) capture network-wide origin-destination demand and are central to traffic engineering, yet accurate whole-matrix forecasting remains challenging when prediction must be performed under the memory, update, and training-budget constraints of online network control.

By Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin, Tai-Yue Li, Nan-Yow Chen, Samuel Yen-Chi Chen

llms diffusion benchmarks

arXiv AI

Jul 21

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).

By Sanjay Chakraborty, Fredrik Heintz

efficiency benchmarks

arXiv Machine Learning

Jun 25

Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning

arXiv:2606. 24933v1 Announce Type: cross Abstract: Recent advances in quantum machine learning have motivated efficient models for sequential data processing.

By Samuel Yen-Chi Chen, Yifeng Peng, Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin, Junghoon Justin Park, Huan-Hsin Tseng, Hsin-Yi Lin, Kuan-Cheng Chen, Chen-Yu Liu, Shinjae Yoo

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 AI

Jun 18

Controllable Quantum Memory Capacity in Quantum Reservoir Networks with Tunable partial-SWAPs

arXiv:2605. 12713v3 Announce Type: replace-cross Abstract: In the field of quantum reservoir computing (QRC), many different computational models and architectures have been proposed.

By Erik L. Connerty, Ethan N. Evans

benchmarks

arXiv Machine Learning

1d ago

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

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

Hugging Face Trending Papers

Jul 14

When Close Enough Is Not Enough: Autoregressive Drift in Quantum Circuit Synthesis

Quantum circuit optimization for fault-tolerant computing requires exact functional equivalence while minimizing expensive non-Clifford resources such as T gates. We study this problem using a compact 44.

llms nlp fine-tuning

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

Jun 9

Adaptive directional gradients for parameterised quantum circuits

arXiv:2606. 09734v1 Announce Type: cross Abstract: Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linearly in the number of trainable parameters and dominates the total shot budget of training at scale.

By Brian Coyle, Snehal Raj, Virag Umathe, El Amine Cherrat, Elham Kashefi

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