Research
Tensor Network Algorithms
Approximate and exact contraction methods for arbitrary tensor networks, graphical models, quantum circuits, and many-body systems. We are interested in algorithms that trade accuracy, memory, and parallelism in a controlled way.
Quantum Circuit Simulation
Classical simulation and sampling methods for large-scale quantum circuits, including random circuit sampling and quantum advantage experiments. This line combines tensor networks, sparse-state methods, and high-performance implementation.
Statistical Mechanics and Learning
Machine-learning-assisted methods for spin glasses, combinatorial optimization, variational inference, and generative models. We use statistical physics to understand when learning and sampling algorithms work.
Quantum Error Correction
Learning-guided decoding and tensor-network perspectives on noisy quantum information processing. Current interests include generative decoders and exact/approximate decoding under realistic circuit-level noise.
High-Performance Scientific Computing
Scalable implementations for physics simulation, tensor contractions, and quantum algorithms. We care about the full path from theory to efficient code on modern computing platforms.
Collaborative Projects
We welcome collaborations with researchers working on quantum algorithms, many-body physics, optimization, scientific machine learning, and high-performance computing.