Running the Dual-PQC GAN on noisy simulators and real quantum hardware

Abstract

In an earlier work, we introduced dual-Parameterized Quantum Circuit (PQC) Generative Adversarial Networks (GAN), an advanced prototype of a quantum GAN. We applied the model on a realistic High-Energy Physics (HEP) use case: the exact theoretical simulation of a calorimeter response with a reduced problem size. This paper explores the dual- PQC GAN for a more practical usage by testing its performance in the presence of different types of quantum noise, which are the major obstacles to overcome for successful deployment using near-term quantum devices. The results propose the possibility of running the model on current real hardware, but improvements are still required in some areas.

6 pages, 5 figures, Proceedings of the 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2021)

References in corpus (4)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Variational Quantum Algorithms
• Noise-Induced Barren Plateaus in Variational Quantum Algorithms
• Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics

Cited by in corpus (3)

• A performance characterization of quantum generative models
• Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection
• Quantum Computing for Data Centric Engineering and Science