Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics

Abstract

Generative models, and Generative Adversarial Networks (GAN) in particular, are being studied as possible alternatives to Monte Carlo simulations. It has been proposed that, in certain circumstances, simulation using GANs can be sped-up by using quantum GANs (qGANs). We present a new design of qGAN, the dual-Parameterized Quantum Circuit(PQC) GAN, which consists of a classical discriminator and two quantum generators which take the form of PQCs. The first PQC learns a probability distribution over N-pixel images, while the second generates normalized pixel intensities of an individual image for each PQC input. With a view to HEP applications, we evaluated the dual-PQC architecture on the task of imitating calorimeter outputs, translated into pixelated images. The results demonstrate that the model can reproduce a fixed number of images with a reduced size as well as their probability distribution and we anticipate it should allow us to scale up to real calorimeter outputs.

References in corpus (10)

• The theory of variational hybrid quantum-classical algorithms
• Parameterized quantum circuits as machine learning models
• Quantum optimization using variational algorithms on near-term quantum devices
• Quantum generative adversarial learning
• Quantum generative adversarial networks
• Quantum Generative Adversarial Networks for Learning and Loading Random Distributions
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Quantum gradient descent for linear systems and least squares
• Quantum generative adversarial network for generating discrete distribution
• Adversarial quantum circuit learning for pure state approximation

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• Machine Learning for Anomaly Detection in Particle Physics
• Style-based quantum generative adversarial networks for Monte Carlo events
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• Unsupervised Quantum Circuit Learning in High Energy Physics
• Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time-Series
• 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
• Protocols for Trainable and Differentiable Quantum Generative Modelling
• Guided Quantum Compression for High Dimensional Data Classification
• Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework
• Running the Dual-PQC GAN on noisy simulators and real quantum hardware
• Quantum Computing for Data Centric Engineering and Science
• Multiqubit state learning with entangling quantum generative adversarial networks
• Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction
• 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