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.
A quantum k-nearest neighbors implementation using magnitude-aware comparisons
The paper introduces a practical quantum encoding technique that allows Euclidean distance calculation between vectors with different L2 norms by appending a single component to each vector. This method is applied to the quantum k‑nearest neighbors (QKNN) problem, offering a simpler and more qubit‑efficient implementation than existing approaches. The authors provide an implementation and benchmark it against a recent Euclidean‑distance QKNN method, showing improved performance in simulations with realistic noise due to the reduced qubit count and more coherent circuits.
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arXiv Machine Learning
Jul 15
Qubit-Efficient Quantum Search for Hyperdimensional Decomposition via Logarithmic Encoding
arXiv:2607. 11936v1 Announce Type: new Abstract: Hyperdimensional Computing (HDC) represents symbols using high-dimensional hypervectors of dimension $D$.
arXiv Machine Learning
Jul 8
Low-Overhead Error-Corrected QCNNs Using Bivariate Bicycle Codes
arXiv:2607. 05724v1 Announce Type: new Abstract: Quantum convolutional neural networks (QCNNs) combine the power of quantum computing and classical CNN for computational speedup in classification tasks.
Hugging Face Trending Papers
Oct 27
Quantum Evolution Kernel for Node-Attributed Graphs
The paper introduces a new method for embedding node‑attributed graphs onto neutral‑atom quantum computers, extending PASQAL’s quantum evolution kernel by adding a node‑level feature encoding using three‑level quantum states. A prototype demonstrates how this embedding can extract correlation‑based information for classification, specifically applied to digit recognition. Experiments, simulated with Pasqal’s Pulser library, show that the proposed approach outperforms traditional Jensen‑Shannon distance‑based methods by using the Wasserstein distance, achieving higher mean accuracy on the digit recognition task.
arXiv Machine Learning
Jul 23
A Multiclass Quantum Aligned Centroid Kernel
arXiv:2607. 19782v1 Announce Type: cross Abstract: Kernel methods are powerful tools in machine learning but commonly used full-Gram kernels face three key limitations: (1) quadratic scaling with training set size; (2) the use of fixed, non-trainable kernels; and (3) the absence of an intrinsic formulation for multiclass classification.
arXiv Machine Learning
Oct 15
A Versatile Variational Quantum Kernel Framework for Non-Trivial Classification
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 Machine Learning
Oct 16
A Quantum Machine Learning Approach to Content-Based Media Recommendation
The paper presents a quantum machine learning approach to content-based media recommendation, mapping BBC iPlayer programme synopses to variational quantum circuits within the Quantum Natural Language Processing framework. The quantum model, trained via supervised pairwise similarity learning, matches a classical BiLSTM baseline in performance while using far fewer parameters (47k vs. 17.8M). Experiments on Quantinuum's System Model H2 confirm that the similarity signal survives realistic noise, demonstrating the feasibility of deploying variational quantum circuits for recommendation tasks.
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
Oct 23
Can Entanglement-Induced Feature-Map Designs Improve Projected Quantum Kernels?
The paper investigates projected quantum kernels (PQKs) and how their performance is often obscured by extensive classical hyperparameter tuning of RBF kernels and SVM regularization. By comparing untuned and tuned PQKs to an RBF baseline, the authors isolate the native inductive bias of PQKs and find that recent designs can be improved by quantum feature maps that use two‑qubit entanglement to capture multivariate correlations in IoT data. The study shows that entanglement‑based topologies provide a stronger native inductive bias, leading to better PQK performance that stems from quantum‑mechanical properties rather than from classical tuning.
arXiv Machine Learning
Sep 10
QGB-W$k$NN: Quantum Granular-Ball Learning for Robust Classification
arXiv:2609.05952v1 Announce Type: new Abstract: Nearest-neighbor classification is widely used in machine learning, yet existing methods often suffer from low computational efficiency and limited rob...
arXiv Machine Learning
Jul 30
Shot-based quantum encoding: a data-loading paradigm for quantum neural networks
arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.
arXiv Machine Learning
Oct 3
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.