Unsupervised Quantum Circuit Learning in High Energy Physics

Abstract

Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. In this work we evaluate the efficacy of using quantum circuit-based generative models to generate synthetic data of high energy physics processes. We use non-adversarial, gradient-based training of quantum circuit Born machines to generate joint distributions over 2 and 3 variables.

arXiv comment13 pages, 15 figures, 4 tables

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Cited by in corpus (11)

• Machine Learning for Anomaly Detection in Particle Physics
• Quantum phase detection generalisation from marginal quantum neural network models
• Trainability barriers and opportunities in quantum generative modeling
• Conditional Born machine for Monte Carlo event generation
• Fitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets
• Jet Discrimination with Quantum Complete Graph Neural Network
• Guided Quantum Compression for High Dimensional Data Classification
• Quantum State-Channel Duality for the calculation of Standard Model scattering amplitudes
• 1 Particle - 1 Qubit: Particle Physics Data Encoding for Quantum Machine Learning
• Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
• Flowing Through Hilbert Space: Quantum-Enhanced Generative Models for Lattice Field Theory