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.
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.
Related stories
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.
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.
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.
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).
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.
arXiv Machine Learning
Jun 25
Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation
arXiv:2606. 24932v1 Announce Type: cross Abstract: Recent advances in quantum computing and machine learning have motivated the development of quantum models for sequential data processing.
arXiv AI
Aug 10
QFCQT: A Chaotically Gated Quantformer Framework for Volatile Time-Series Forecasting
arXiv:2608. 07363v1 Announce Type: new Abstract: Forecasting non-stationary time series remains difficult due to long-range dependencies, local volatility bursts, structural shifts, and nonlinear oscillatory behaviors.
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.
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.
arXiv Machine Learning
Aug 11
Multi-Head Residual-Gated DeepONet for Coherent Nonlinear Wave Dynamics
arXiv:2604. 11972v2 Announce Type: replace Abstract: Coherent nonlinear wave dynamics are often strongly shaped by a compact set of physically meaningful descriptors of the initial state.
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.