XTRIPODS: Advancing Quantum Data Science Research and Education: Resilient Quantum Learning in NISQ era

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Data science-based approaches have empowered popular technologies, including deep learning and large language models, leading to significant advancements in artificial intelligence. However, in the post-Moore’s Law era, the limitations of semiconductor fabrication, combined with the continuous growth of data sizes, have hindered further progress. Simultaneously, the rapid evolution of quantum computing has ushered in a new era, holding immense potential. This has sparked interest in quantum-based data science and learning, with the anticipation that certain systems could provide a quantum speedup. This project will harness the multi-disciplinary expertise from our NSF TRIPODS collaboration to develop noise-aware and resilient quantum learning systems. This project bridges the gap between research communities in traditional data science and quantum computing, thereby introducing classical data science researchers to the opportunities in quantum data science (QuanDS). Furthermore, this project will contribute to developing a skilled workforce, well-versed in cutting-edge AI and equipped to navigate the unique challenges of this emerging field in QuanDS.This project develops a quantum learning system using a data-driven approach that integrates static-dynamic combined circuit analysis. This method combines static metrics with the inherent dynamic noises of quantum systems by developing a set of noise-involved evaluation metrics, such as circuit sensitivity indicator. A topology-aware resilient circuit transpilation mechanism tailored to quantum learning will then be developed to optimize the execution on noisy intermediate-scale quantum (NISQ) era hardware under quantum noise. Additionally, the project provides a holistic analysis of the vulnerability in distributed quantum learning by reasoning the strategic behavior of the attacker to quantum computing nodes, and devises resilient learning countermeasures in the distributed quantum-classical learning paradigm. Furthermore, it develops educational modules in quantum control systems combined with resilient quantum learning techniques. The comprehensive outreach activities will be developed to integrate QuanDS research and education.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.