arXiv:2606. 07766v1 Announce Type: cross Abstract: We present a quantum--classical hybrid pipeline for polarimetric material classification that casts this as a point-matching problem.
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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: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: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).
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...
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%.