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Articles by Zhide Lu
Demonstration of low-overhead quantum error correction codes - Nature Physics
Abstract Quantum computers hold the potential to surpass classical computers in solving complex computational problems. The fragility of quantum information and the error-prone nature of quantum operations necessitate the use of quantum error correction codes to achieve fault-tolerant quantum computing. However, most codes that have been demonstrated so far suffer from low encoding efficiency, and their scalability is hindered by prohibitively high resource overheads.
Topological prethermal strong zero modes on superconducting processors - Nature
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Abstract Symmetry-protected topological phases1,2,3,4 cannot be described by any local order parameter and are beyond the conventional symmetry-breaking model5. They are characterized by topological boundary modes that remain stable under symmetry respecting perturbations1,2,3,4,6,7,8.
By Feitong Jin, Si Yue Jiang, Xuhao Zhu, Zehang Bao, Fanhao Shen, Ke Wang, Zitian Zhu, Shibo Xu, Zixuan Song, Ziqi Tan, Yaozu Wu, Yu Gao, Ning WANG, Yiren Zou, Aosai Zhang, Tingting Li, Jiarun Zhong, Jinfeng Deng, Hang Dong, Pengfei Zhang, Weikang Li, Zhide Lu, Zheng-Zhi Sun, Hekang Li, Junxiang Zhang, Zhen J Wang, Qiujiang Guo, Thomas Iadecola
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Nature
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Non-Hermitian Fermi-Dirac Distribution in Persistent Current Transport
Persistent currents circulate continuously without requiring external power sources. Here, we extend their theory to include dissipation within the framework of non-Hermitian quantum Hamiltonians. Using Green’s function formalism, we introduce a non-Hermitian Fermi-Dirac distribution and derive an analytical expression for the persistent current that relies solely on the complex spectrum.
Deep quantum neural networks on a superconducting processor - Nature Communications
Abstract Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of quantum machine learning. In this work, we report an experimental demonstration of training deep quantum neural networks via the backpropagation algorithm with a six-qubit programmable superconducting processor.
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