A Full Quantum Generative Adversarial Network Model for High Energy Physics Simulations

Understand

The prospect of quantum computing with a potential exponential speed-up compared to classical computing identifies it as a promising method in the search for alternative future High Energy Physics (HEP) simulation approaches.

• HEP simulations, such as employed at the Large Hadron Collider at CERN, are extraordinarily complex and require an immense amount of computing resources in hardware and time.
• For some HEP simulations, classical machine learning models have already been successfully developed and tested, resulting in several orders of magnitude speed-up.
• In this research, we proceed to the next step and explore whether quantum computing can provide sufficient accuracy, and further improvements, suggesting it as an exciting direction of future investigations.