Search Results for author: Chu Guo

Found 6 papers, 1 papers with code

Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics

no code implementations17 Apr 2024 Heitor P. Casagrande, Bo Xing, William J. Munro, Chu Guo, Dario Poletti

This new approach can accurately learn probabilistic cellular automata processes in different conditions, even when the process is a probabilistic mixture of different chaotic rules.

Tensor Networks

NNQS-Transformer: an Efficient and Scalable Neural Network Quantum States Approach for Ab initio Quantum Chemistry

no code implementations29 Jun 2023 Yangjun Wu, Chu Guo, Yi Fan, Pengyu Zhou, Honghui Shang

Neural network quantum state (NNQS) has emerged as a promising candidate for quantum many-body problems, but its practical applications are often hindered by the high cost of sampling and local energy calculation.

Variational Monte Carlo

Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation

no code implementations16 Nov 2022 He-Liang Huang, Xiao-Yue Xu, Chu Guo, Guojing Tian, Shi-Jie Wei, Xiaoming Sun, Wan-su Bao, Gui-Lu Long

To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications.

Benchmarking

Quantum inspired K-means algorithm using matrix product states

no code implementations11 Jun 2020 Xiao Shi, Yun Shang, Chu Guo

Matrix product state has become the algorithm of choice when studying one-dimensional interacting quantum many-body systems, which demonstrates to be able to explore the most relevant portion of the exponentially large quantum Hilbert space and find accurate solutions.

Computational Physics Quantum Physics

A scheme for automatic differentiation of complex loss functions

no code implementations2 Mar 2020 Chu Guo, Dario Poletti

For a real function, automatic differentiation is such a standard algorithm used to efficiently compute its gradient, that it is integrated in various neural network frameworks.

BIG-bench Machine Learning

Variational Quantum Circuits for Quantum State Tomography

1 code implementation16 Dec 2019 Yong Liu, Dongyang Wang, Shichuan Xue, Anqi Huang, Xiang Fu, Xiaogang Qiang, Ping Xu, He-Liang Huang, Mingtang Deng, Chu Guo, Xuejun Yang, Junjie Wu

We demonstrate our method by performing numerical simulations for the tomography of the ground state of a one-dimensional quantum spin chain, using a variational quantum circuit simulator.

Quantum Machine Learning Quantum State Tomography

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