arXiv:2606. 14822v1 Announce Type: cross Abstract: Recent advances in Machine Learning have transformed numerous industrial sectors, yet classical paradigms face fundamental limitations: rapidly growing data volumes, rising computational costs, significant energy consumption, and the physical scaling limits of conventional hardware architectures.
arXiv:2606. 26312v1 Announce Type: cross Abstract: Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning compact, structured representations.
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.
The paper evaluates hybrid quantum‑classical machine learning for predicting reduced‑order spatiotemporal brain deformation fields. Using Proper Orthogonal Decomposition to compress high‑dimensional displacement data, the authors compare static temporal‑to‑latent regression and autoregressive latent forecasting models. Classical neural networks outperform all quantum variants, though enhanced quantum circuits improve over minimal ones, indicating that classical architectures still hold a clear advantage in fidelity and stability for this task.
arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.
arXiv:2412. 09486v2 Announce Type: replace-cross Abstract: The literature reflects a mutually beneficial relationship between machine learning and quantum computing, where progress in one field frequently drives improvements in the other.
arXiv:2608. 11884v1 Announce Type: cross Abstract: Quantum generative adversarial networks (QGANs) have attracted increasing attention for image generation using parameterized quantum circuits.
Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment introduces Sim‑HVQC, a hybrid deep quantum neural network that integrates an adaptive, parameter‑free SimAM weighting module with classical feature extraction to retain class‑discriminative information before encoding into a Variational Quantum Circuit. Unlike prior work limited to binary classification, this framework is trained and evaluated on multiple multi‑class datasets such as MNIST, KMNIST, Fashion‑MNIST, and EMNIST. The study highlights reproducibility, parameter efficiency, and interpretability through multi‑seed evaluation, parameter analysis, and latent/quantum feature inspection, with source code publicly available on GitHub.
arXiv:2511.10831v3 Announce Type: replace Abstract: Quantum kernel methods are a promising branch of quantum machine learning, but their per- formance across data types and encoding choices remains i...
arXiv:2603.06755v3 Announce Type: replace Abstract: We propose a quantum implicit neural representation (QINR)-based autoencoder (AE) and variational autoencoder (VAE) for image reconstruction and ge...
arXiv:2604. 26675v2 Announce Type: replace-cross Abstract: We investigate variational quantum classifiers (VQCs) for land-cover classification from multispectral satellite imagery, adopting a feature-map perspective in which the quantum circuit defines a nonlinear data embedding while the readout determines how this representation is exploited.
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).