This is a graduate-level course on modern novel developments of quantum computing.Â
Learning Objectives
Build a strong foundation in quantum computing concepts and their implementations, using linear algebra as the language of analysis and computation.
Learn to design and program variational quantum algorithms, and use classical optimization techniques, such as gradient descent, to minimize loss functions effectively.
Develop skills to implement variational quantum algorithms for quantum optimization, including the Quantum Approximate Optimization Algorithm (QAOA), and tackle data-driven tasks in quantum machine learning.
Explore the principles of quantum error correction, focusing on the stabilizer formalism and foundational concepts of surface codes.
Learn to use STIM for simulating Clifford noise models in quantum error correction codes, enabling robust error analysis and testing.
Acquire foundational knowledge in post-quantum cryptography, implementing public key encryption and digital signatures for NIST-standard cryptographic systems such as Crystal-Kyber, Falcon, Crystal-Dilithium, and SPHINICS+.
See the corresponding materials in Canvas.
Dr. Junyu Liu, Department of Computer Science, School of Computing & Information, University of Pittsburgh.
junyuliu@pitt.edu