6 citations · 11 across the 7 of their papers we have counts for
7 papers
Branch-and-Bound Tensor Networks for Exact Ground-State Characterization
Yijia Wang, Xuanzhao Gao, Pan Zhang +2
Characterizing the ground-state properties of disordered systems, such as spin glasses and combinatorial optimization problems, is fundamental to science and engineering. However,…
Integrating Neural Networks and Tensor Networks for Computing Free Energy
Hanyan Cao, Yijia Wang, Feng Pan +1
Computing free energy is a fundamental problem in statistical physics. Recently, two distinct methods have been developed and have demonstrated remarkable success: the tensor-netwo…
Generative Decoding for Quantum Error-correcting Codes
Hanyan Cao, Feng Pan, Dongyang Feng +2
Efficient and accurate decoding of quantum error-correcting codes is essential for fault-tolerant quantum computation, however, it is challenging due to the degeneracy of errors, t…
Automated Discovery of Branching Rules with Optimal Complexity for the Maximum Independent Set Problem
Xuan-Zhao Gao, Yi-Jia Wang, Pan Zhang +1
The branching algorithm is a fundamental technique for designing fast exponential-time algorithms to solve combinatorial optimization problems exactly. It divides the entire soluti…
qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformers
Hanyan Cao, Feng Pan, Yijia Wang +1
We propose a general framework for decoding quantum error-correcting codes with generative modeling. The model utilizes autoregressive neural networks, specifically Transformers, t…
Tensor Network Message Passing
Yijia Wang, Yuwen Ebony Zhang, Feng Pan +1
When studying interacting systems, computing their statistical properties is a fundamental problem in various fields such as physics, applied mathematics, and machine learning. How…
Rediscovery of Numerical Lüscher's Formula from the Neural Network
Yu Lu, Yi-Jia Wang, Ying Chen +1
We present that by predicting the spectrum in discrete space from the phase shift in continuous space, the neural network can remarkably reproduce the numerical Lüscher's formula t…