معرفی
Daniel Liang is a Postdoctoral Researcher jointly supervised by Dr. Nai-Hui Chia at Rice University and Dr. Fang Song at Portland State University. His research bridges quantum computing, complexity theory, and learning theory, with a focus on efficient information extraction from quantum systems.
- PhD in Computer Science from University of Texas at Austin
- Bachelor of Science in Engineering from Cornell University in Computer Science and Engineering Physics
His work explores time-efficient algorithms for quantum systems, aiming to benchmark quantum computers, discover new physics, and understand quantum computation limits. Key areas include stabilizer states, quantum tomography, and connections between learning theory and circuit complexity.
Recent publications highlight advances in quantum state learning, stabilizer complexity, and pseudorandomness, with applications in tomography, optimization algorithms, and computational complexity reductions.
- ITCS 2023 Best Student Paper Award
Collaborations span Rice University, Portland State University, and institutions like UT Austin, with interdisciplinary contributions to quantum algorithms and theoretical computer science.
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