Faculty
Xulong Tang (CS), Jun Yang (ECE)
PhD Students
Kaiwen Zhao, Aditya Pawar
Description
Quantum computing has undergone significant developments and established its supremacy in many application domains. While quantum hardware is accessible to the public through the cloud environment, a robust and efficient quantum circuit simulator is necessary to investigate the constraints and foster quantum computing development, such as quantum algorithm development and quantum device architecture exploration. In this project, we aim to develop quantum circuit simulation schemes with comprehensive optimizations to allow efficient and scalable QCS.
The second direction in our effort focus on studying the types of errors and mitigate them in quantum circuits. Recent studies suggested that the errors encountered during the execution of a quantum circuit do not necessarily cause random outputs but rather have certain patterns. These patterns are observed in low-qubit machines, and can be leveraged to develop error filtering mechanism for the output results. However, it is unclear if high-qubit machines would present similar or different patters and same filtering strategies would still hold. We aim to uncover more such characteristics and develop new techniques to denoise the quantum circuit output.
Publications
- Yilun Zhao, Yanan Guo, Yuan Yao, Amanda Dumi, Devin M Mulvey, Shiv Upadhyay, Youao Zhang, Keneth D Jordan, Jun Yang, Xulong Tang "Q-GPU: A Recipe of Optimizations for Quantum Circuit Simulation Using GPUs," in Proceedings of the 28th IEEE International Symposium on High-Performance Computer Architecture, 2022.