CAREER: Learning Quantum States: Robustness, Noise, and Novel Algorithms

In the coming decades, quantum computing is expected to expand the frontiers of scientific computing by enabling computational tasks that are otherwise difficult to perform. A fundamental task in this area is learning the properties of quantum states, with applications to verifying quantum devices, developing quantum algorithms, ensuring the security of quantum communication, and probing the foundations of quantum mechanics. However, quantum systems are highly sensitive to noise, making it difficult to build reliable quantum computers in practice. This project develops new methods for learning quantum states that remain reliable even in the presence of noise. Understanding how to mitigate noise is essential for designing scalable, fault-tolerant quantum hardware and enabling reliable implementation of advanced quantum algorithms. This project will also support the education and training of students and contribute to educational activities, including summer camps and lecture materials, that help prepare the future quantum information workforce. By advancing fundamental research and education in quantum information science, this work will contribute to the continued development of quantum technologies. This project develops new theoretical frameworks and algorithms for learning quantum states in practical settings. The research will design and analyze algorithms that are robust to noise and data fluctuations and characterize their performance and limitations. It will develop methods for classifying quantum states, analyzing their behavior in noisy environments, and exploring their applications. The project will also develop methods for learning quantum states from their preparation devices, with an emphasis on identifying when such tasks can be performed optimally. These approaches combine techniques from quantum information theory and optimization to produce algorithms with provable performance guarantees and practical applicability. These results will deepen understanding of quantum state learning and inform the design of efficient and reliable quantum algorithms suitable for use on real quantum hardware. 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. NSF Award ID: 2542721 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT,01002930DB NSF RESEARCH & RELATED ACTIVIT,01003031DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Jamie Sikora | Institution: Virginia Polytechnic Institute and State University, BLACKSBURG, VA | Award Amount: $413,253 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2542721 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2542721.html