QHack2023 (Pennylane)
Accelerating Noisy Algorithm Research with PennyLane-Lightning and NVIDIA cuQuantum SDK
This project was a submission for the Quantum Computing Hackathon QHack2023 by Pennylane in the category of Nvidia, and was awarded the first place worldwide.
It was an equal collaboration between Lion Frangoulis (lion.frangoulis@tum.de), Cristian Emiliano Godinez Ramirez (cristian.godinez@tum.de), Emily Haworth (ge96puk@mytum.de), and Aaron Sander (aaron.sander@tum.de) from the Technical University of Munich.
- Project repository.
- PennyLane announcement.
- Nvidia announcement.
Abstract:
In this project, we consider the challenges of simulating noisy quantum algorithms, which are known to require significant computational resources. We address this issue by leveraging the GPU tools available in Xanadu’s PennyLane-Lightning-GPU and NVIDIA’s cuQuantum SDK, which enable us to scale up our simulations and gain deeper insights into the impact of noise on quantum algorithms. Our analysis sheds light on the general effects of noise on simulation and identifies areas where it can accelerate the simulation of open quantum systems and ground state optimization. Through our work, we hope to contribute to a better understanding of how to effectively simulate noisy quantum algorithms, which could have far-reaching implications for quantum computing and finding NISQ era use cases.