arXiv Machine Learning
Jun 9
Quantum feature-map learning with reduced resource overhead
arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.
Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification
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.
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arXiv Machine Learning
Sep 22
Comparative Study of Quantum and Classical Machine Learning Models in Binary Classification
arXiv:2609.23476v1 Announce Type: cross Abstract: A potential path forward is Quantum Machine Learning (QML), which aims to leverage quantum computing in conjunction with classical machine learning t...
arXiv Machine Learning
Jul 13
Is data-efficient learning feasible with quantum models?
arXiv:2508. 19437v2 Announce Type: replace-cross Abstract: The importance of analyzing nontrivial datasets when testing quantum machine learning (QML) models is becoming increasingly prominent in literature, yet a cohesive framework for understanding dataset characteristics remains elusive.
arXiv AI
Jul 21
Do We Really Need Quantum Machine Learning?: A Multidimensional Empirical Study
arXiv:2605. 27923v2 Announce Type: replace-cross Abstract: The rapid growth of computer vision and increasingly complex image recognition tasks has exposed fundamental computational limitations of classical machine learning models, motivating the exploration of quantum computing as an emerging new paradigm.
arXiv Machine Learning
Jul 8
Benign Overfitting with Quantum Kernels
arXiv:2503. 17020v2 Announce Type: replace-cross Abstract: Kernel methods compare inputs through feature maps.
arXiv AI
Sep 25
Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment
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 Machine Learning
Jun 3
QUIVER: Quantum-Informed Views for Enhanced Representations in Large ML Models
arXiv:2606. 02785v1 Announce Type: new Abstract: Large machine learning models benefit substantially from multimodal inputs that provide a complementary view of the same example.
arXiv Machine Learning
Sep 17
Fourier Analysis of Parametrized Interactive Quantum Classifiers
The paper derives a closed‑form expression for the reduced quantum channel of a single‑qubit Interactive Quantum Classifier (IQC), revealing that Hamiltonian parameters directly control the constant, sine, and cosine components of the classifier output. This Fourier interpretation motivates a generalized family of Hamiltonian encodings, including matrix‑parameterized environmental Hamiltonians that produce non‑separable Fourier structures. Numerical experiments on synthetic and real‑world datasets demonstrate that these generalized encodings improve classification performance on several nonlinear benchmarks, with a simpler four‑parameter extension achieving comparable results with fewer trainable parameters.
arXiv AI
Aug 10
Investigating Quantum-Embedded Transformers on Classical Datasets for Cross-Modality Classification
arXiv:2608. 06846v1 Announce Type: cross Abstract: We test whether a parameterized quantum circuit (PQC) improves a hybrid quantum-classical model's performance on classical datasets, using an interface-matched classical map as the control while holding all other components fixed.
arXiv Machine Learning
1d ago
Neural Fourier Surrogates for Data Reuploading Quantum Neural Networks
The paper introduces Neural Fourier Surrogates (NFS), a stochastic classical neural network that learns coefficients over a finite Fourier series support similar to quantum neural networks (QNNs). By testing on tabular benchmark datasets, NFS demonstrates competitive classification performance against established classical baselines and data‑reuploading QNNs. The authors also compare the learned Fourier spectra of QNNs and NFS on synthetic data, positioning NFS as a natural classical baseline for evaluating QNN performance.
arXiv Machine Learning
Jun 26
Tailor Made Embeddings for Quantum Machine Learning
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.
arXiv Machine Learning
Jul 20
From hyperplanes to hyperellipsoids: characterizing the inherent interpretability of linear and single-qubit mixed-state binary classification models
arXiv:2607. 15433v1 Announce Type: new Abstract: We characterize and compare the inherent interpretability offerings of a standard linear model with a single qubit mixed state model for the task of supervised binary classification.