Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments
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. Deployment of QML in real-time collider applications such as trigger systems requires the ability to emulate and compile quantum circuits classically, then synthesize the resulting quantum gates onto low-latency hardware accelerators, namely field-programmable gate arrays (FPGAs). We present a study of variational quantum autoencod
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- PossiblePossibly related (embedding) · 52%Path Integral Quantum Mechanics in the Era of Machine Learning - AIP Publishing LLC →
- LinkedLinked via arxiv author · 85%Ivan Ge →
“Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments”
- LinkedLinked via arxiv author · 85%Sagar Addepalli →
“Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments”
- LinkedLinked via arxiv author · 85%Abhilasha Dave →
“Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments”
- LinkedLinked via arxiv author · 85%Julia Gonski →
“Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments”