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.
By Mina Abbaszadeh, Ouissal Moumou, Anna Pearson, Bob Coecke, Rob Cooper, Alessandro Piscopo, Mehrnoosh Sadrzadeh
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...
By Suzhen Yuan, Dehang Chen, Lifeng Shen, Shuyin Xia, Jeremiah D. Deng
arXiv:2607. 01329v1 Announce Type: cross Abstract: The geometric and topological structure of quantum cost landscapes (QCLs) governs the optimization and thus the predictive power of variational quantum algorithms (VQAs).
By Felix J. Beckmann, Jo\~ao F. Bravo
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: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...
By Jiang Yuhan, Matthew Otten
The paper introduces a quantum score‑matching framework that extends classical score matching to quantum states, addressing challenges posed by noncommuting density operators. It demonstrates that this method can learn thermal (Gibbs) states without extra state preparation, achieving optimal sample complexity in high‑temperature regimes for local Hamiltonians. Numerical tests and experiments on IBM quantum hardware confirm the approach’s effectiveness and NISQ‑friendly performance, reducing Hamiltonian‑parameter error from 64% to about 10%.
By Yulong Dong, Jiaqi Leng
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.
By Oliver Knitter, Jonathan Mei, Sang Hyub Kim, Chi Chen, Masako Yamada, Martin Roetteler
The paper presents a hybrid quantum‑classical pipeline that uses neutral‑atom reservoir computing for medical image classification, specifically binary polyp detection. It incorporates a guided auto‑encoder to reduce dimensionality and a differentiable surrogate model to bypass the gradient barrier of quantum measurements, enabling end‑to‑end backpropagation. The resulting compact latent representations are encoded as pulse detuning parameters in a Rydberg Hamiltonian, producing quantum embeddings that feed a linear classifier, and simulations show improved performance over traditional PCA or unguided auto‑encoder methods.
By Nuno Batista, Ana Morgado, Oscar Ferraz, Sagar Silva Pratapsi, Jorge Lobo, Gabriel Falcao
arXiv:2609.06016v1 Announce Type: new
Abstract: Quantum clustering aims to exploit quantum feature representations to uncover complex data structures beyond conventional Euclidean geometry. Yet this...
By Suzhen Yuan, Qilin Xie, Lifeng Shen, Shuyin Xia, Jermiah D. Deng, Guoying Wang
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.
By Aldo Lamarre, Dominik \v{S}afr\'anek
Most multimodal learning methods improve how heterogeneous representations are aligned and fused, while post-fusion enhancement remains less explored. We propose Parallel Quantum Feature Augmentation (PQFA), a hybrid quantum-classical framework that applies multiple shallow variational quantum circuits to fused multimodal features.
arXiv:2607. 13466v1 Announce Type: new Abstract: Most multimodal learning methods improve how heterogeneous representations are aligned and fused, while post-fusion enhancement remains less explored.
By Mingzhu Wang, Yun Shang