Unsupervised Quantum Circuit Learning in High Energy Physics
arXiv:2203.03578 · doi:10.1103/PhysRevD.106.096006
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.
13 pages, 15 figures, 4 tables
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Cited by in corpus (11)
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- Quantum phase detection generalisation from marginal quantum neural network models
- Trainability barriers and opportunities in quantum generative modeling
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- 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