Quantum Journal

Qkabrine: A Joint Architecture, Encoding, and Hyperparameter Search Framework for Quantum Machine Learning

Building a quantum machine learning (QML) model competitive with a classical baseline currently requires a practitioner to separately choose a circuit architecture, a data-encoding scheme, a model paradigm (kernel versus variational), and a set of training hyperparameters, then verify after the fact that the chosen circuit is even trainable. Existing QML libraries provide the primitives for this but not the search, and existing classical AutoML...

How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection

Quantum machine learning (QML) for network intrusion detection (NIDS) is routinely reported to reach near-perfect accuracy, yet the most rigorous studies find that well-tuned classical models remain competitive, and that apparent quantum gains may be artefacts of classical dimensionality reduction and implicit regularisation rather than genuine quantum effects. We ask not whether a quantum model can post a high accuracy, but how quantum the adva...

Fixed-order postselected CHSH reference acquisition for parity-constrained spatial-mode qubits on a commercial cloud photonic processor

We report a fixed-order Clauser-Horne-Shimony-Holt (CHSH) acquisition and reporting protocol for two encoded photonic qubits on Quandela's commercial, cloud-accessible Belenos processor, executed end-to-end by external users through the public cloud interface. Each logical qubit is a two-dimensional spatial-mode subspace in the seven-dimensional zero-sum sector of an eight-mode single-photon register, and a target postselected linear-optical con...