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.
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.
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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.
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 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.
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
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...
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.
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.
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
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 Machine Learning
Sep 14
Trainability-Oriented Hybrid Quantum Regression via Geometric Preconditioning and Curriculum Optimization
The paper introduces a hybrid quantum–classical regression framework that uses a lightweight classical embedding as a learnable geometric preconditioner to improve the conditioning of a downstream variational quantum circuit. It further incorporates a curriculum optimization protocol that gradually increases circuit depth and switches from SPSA-based exploration to Adam-based fine‑tuning. Experiments on PDE‑informed and standard regression datasets show that this approach consistently outperforms pure QNN baselines, yielding more stable convergence and reduced structured errors, especially in data‑limited regimes.
arXiv Machine Learning
Sep 17
Learning to Program Adaptive Non-Local Observables for Machine Learning
The paper introduces QFWP-ANO, a quantum neural network architecture that uses a classical hypernetwork to program variational quantum circuit parameters and non‑local observables conditioned on each input. Unlike existing adaptive non‑local observable (ANO) methods that learn a single static observable, QFWP-ANO dynamically adapts to each input. Experiments on multivariate time‑series forecasting and reinforcement learning tasks show that QFWP-ANO outperforms traditional ANO‑based VQCs and other strong baselines, achieving the lowest mean‑squared error in most settings.