ExpandQISE: Track 1: Quantum Compilation: Elevating Performance and Scalability with Input Adaptivity and Machine Learning
- Institution
- Rochester Institute of Tech, NY
- Principal investigator
- Sonia Lopez Alarcon
- Funding
- $793K
- Duration
- 2024–2027
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Abstract
Non-technical Abstract: Quantum computing holds significant promise for transforming various fields, including drug development and financial modeling. However, the full potential of quantum computing is hindered by the complexities involved in the efficient routing of qubits an essential process for enabling communication between qubits in quantum hardware. This project addresses this critical challenge by developing advanced quantum compilation techniques, focusing on improving the scalability and reliability of qubit routing. Additionally, the project emphasizes diversity in the quantum computing field by promoting peer mentoring and outreach activities at different educational levels. The proposed activities aim to inspire and educate the next generation of quantum scientists, with a particular focus on increasing the participation of women and minorities in this cutting-edge area of research. Technical Abstract: The project tackles the dual problem of routing among physical qubits and composing logical qubits within the context of Quantum Error Correction (QEC) and Noisy Intermediate-Scale Quantum (NISQ) computing. It proposes innovative solutions to optimize qubit routing, which is crucial for achieving large-scale quantum entanglement. The research is divided into two main thrusts: (1) Utilizing machine learning (ML) techniques to identify and synthesize the most effective routing algorithms tailored to specific quantum devices and goals. This involves training ML models to select optimal parameters and optimization functions. (2) Exploring alternative quantum circuit representations, such as hypergraph circuit descriptions and ZX-calculus, to handle the scalability of routing problems and enhance fault tolerance. By addressing these challenges, the project aims to significantly advance quantum compiler design, making it more adaptable to the evolving demands of quantum hardware, and enhancing the overall performance and scalability of quantum computing systems. This award was jointly funded by the Directorate for Mathematical and Physical Sciences, Office of Strategic Initiatives; and the Directorate for Computer and Information Science and Engineering, Division of Computing and Communication Foundations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Research team
- Sonia Lopez AlarconPI
- Yufei Ding
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