arXiv Machine Learning July 23, 2026 By Ivan Ge, Sagar Addepalli, Abhilasha Dave, Julia Gonski

Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments

Read the original on arXiv Machine Learning →

arXiv:2607. 20302v1 Announce Type: new Abstract: Quantum machine learning (QML) algorithms in high energy physics (HEP) can efficiently represent and leverage long-range, high-order correlations in high-dimensional collider data, potentially with fewer parameters and favorable scaling relative to classical models.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.

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Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml

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Transpiler Autotuning with Predictive Models for Quantum Circuit Optimization

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Quantum Machine Learning for Industrial Applications

arXiv:2606. 14822v1 Announce Type: cross Abstract: Recent advances in Machine Learning have transformed numerous industrial sectors, yet classical paradigms face fundamental limitations: rapidly growing data volumes, rising computational costs, significant energy consumption, and the physical scaling limits of conventional hardware architectures.

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arXiv:2607. 07634v1 Announce Type: cross Abstract: Time series analysis plays a vital role across a wide range of scientific and engineering domains but poses substantial computational challenges.

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