5 papers · 1 filter
Learning to Decode Concatenated Quantum Codes with Hierarchical Message Passing
Jiahui Wu, Chao Zhang, Zipeng Wu +1
We introduce a neural message-passing framework for decoding general concatenated stabilizer codes. Soft beliefs propagate bidirectionally across concatenation levels, and lightwei…
Bidirectional Decoding for Concatenated Quantum Hamming Codes
Chao Zhang, Zipeng Wu, Jiahui Wu +1
High-rate concatenated quantum codes offer a promising pathway toward fault-tolerant quantum computation, yet designing efficient decoders that fully exploit their error-correction…
Investigating Pure State Uniqueness in Tomography via Optimization
Jiahui Wu, Zheng An, Chao Zhang +3
Quantum state tomography (QST) is crucial for understanding and characterizing quantum systems through measurement data. Traditional QST methods face scalability challenges, requir…
Dual-Capability Machine Learning Models for Quantum Hamiltonian Parameter Estimation and Dynamics Prediction
Zheng An, Jiahui Wu, Zidong Lin +3
Recent advancements in quantum hardware and classical computing simulations have significantly enhanced the accessibility of quantum system data, leading to an increased demand for…
Unified Quantum State Tomography and Hamiltonian Learning Using Transformer Models: A Language-Translation-Like Approach for Quantum Systems
Zheng An, Jiahui Wu, Muchun Yang +2
Schrödinger's equation serves as a fundamental component in characterizing quantum systems, wherein both quantum state tomography and Hamiltonian learning are instrumental in compr…