arXiv Machine Learning August 3, 2026 By Sathwik Narkedimilli, N V Saran Kumar, Aswath Babu H, Manjunath K Vanahalli, Manish M, Aik Beng Ng, Vinija Jain, Aman Chadha

A Novel XAI-Enhanced Quantum Adversarial Networks for Velocity Dispersion Modeling in MaNGA Galaxies

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arXiv:2510. 24598v2 Announce Type: replace Abstract: Current quantum machine learning approaches often face challenges balancing predictive accuracy, robustness, and interpretability.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 4

QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits

arXiv:2604. 10933v2 Announce Type: replace-cross Abstract: Deep neural networks remain highly vulnerable to adversarial perturbations, limiting their reliability in security- and safety-critical applications.

By Navid Azimi, Aditya Prakash, Yao Wang, Li Xiong

safety

arXiv Machine Learning

Jul 16

Towards quantum machine learning for assessing the resilience of post-quantum cryptography

arXiv:2607. 13722v1 Announce Type: cross Abstract: The potential capabilities of quantum computers motivated the development of cryptographic protocols suitable for securing communication against adversaries with access to large fault-tolerant quantum computers.

By Jaros{\l}aw A. Miszczak

safety

arXiv Computer Vision

4d ago

Quantum Fidelity Landscape-Guided Prior Calibration for Single-Circuit QGAN Image Generation

Quantum Fidelity Landscape-Guided Prior Calibration for Single-Circuit QGAN Image Generation explores a simpler pixel‑level, end‑to‑end approach to quantum generative adversarial networks (QGANs) that avoids patch‑based decomposition. The authors introduce the Quantum Fidelity Landscape (QFL) as an invariant pairwise‑fidelity structure preserved under shared unitary transformations, and use it to calibrate the quantum prior before adversarial training. Their BasicQGAN framework aligns the prior‑induced QFL with the data‑induced QFL, achieving stable, effective image generation on small‑scale grayscale datasets while requiring fewer qubits and trainable parameters than patch‑based quantum generators.

By Xue Yang, Rigui Zhou, Dax Enshan Koh, Siong Thye Goh, Yitao Tang, ShiZheng Jia, Young-Wook Cho, Hongyu Chen

diffusion safety

arXiv AI

Aug 12

Regression and Classification with Single-Qubit Quantum Neural Networks

arXiv:2412. 09486v2 Announce Type: replace-cross Abstract: The literature reflects a mutually beneficial relationship between machine learning and quantum computing, where progress in one field frequently drives improvements in the other.

By Leandro C. Souza, Bruno C. Guingo, Gilson Giraldi, Renato Portugal

arXiv AI

Jul 2

When AI meets quantum information: A comprehensive review

arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.

By Min Chen, Yu Gan, Xin Jin, Yuqing Li, Junqi Wang, Zeguan Wu, Yunfei Wang, Bingzhi Zhang, Priyam Srivastava, Tianlong Chen, Ankit Kulshrestha, Yuan Liu, Juan Jos\'e Mendoza-Arenas, Kaushik P. Seshadreesan, Sarvagya Upadhyay, Xueyue Zhang, Quntao Zhuang, Junyu Liu

Hugging Face Trending Papers

Jul 13

Input-Aware Dynamic Backdoor Attack Against Quantum Neural Networks

Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks remain insufficiently understood. Studies have shown that QNNs are vulnerable to backdoor attacks, yet existing quantum backdoors mostly rely on a fixed trigger shared by all poisoned inputs.

fine-tuning