Search Results - "Cao, Chenfeng"

Carbon support surface effects in the catalytic performance of Ba-promoted Ru catalyst for ammonia synthesis by Lin, Bingyu, Guo, Yunjie, Cao, Chenfeng, Ni, Jun, Lin, Jianxin, Jiang, Lilong

“…[Display omitted] Carbon-supported Ru catalyst is arguably the most promising applications of Ru catalysts in ammonia synthesis. However, the insufficient…”
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Quantum variational learning for quantum error-correcting codes by Cao, Chenfeng, Zhang, Chao, Wu, Zipeng, Grassl, Markus, Zeng, Bei

“…Quantum error correction is believed to be a necessity for large-scale fault-tolerant quantum computation. In the past two decades, various constructions of…”
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Accelerated variational quantum eigensolver with joint Bell measurement by Cao, Chenfeng, Yano, Hiroshi, Nakagawa, Yuya O.

“…The variational quantum eigensolver (VQE) stands as a prominent quantum-classical hybrid algorithm for near-term quantum computers to obtain the ground states…”
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Quantum imaginary time evolution steered by reinforcement learning by Cao, Chenfeng, An, Zheng, Hou, Shi-Yao, Zhou, D. L., Zeng, Bei

“…The quantum imaginary time evolution is a powerful algorithm for preparing the ground and thermal states on near-term quantum devices. However, algorithmic…”
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Speedup of the quantum adiabatic algorithm using delocalization catalysis by Cao, Chenfeng, Xue, Jian, Shannon, Nic, Joynt, Robert

“…We propose a method to speed up the quantum adiabatic algorithm using catalysis by many-body delocalization. This is applied to random-field antiferromagnetic…”
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Quantum approximate optimization algorithm with adaptive bias fields by Yu, Yunlong, Cao, Chenfeng, Dewey, Carter, Wang, Xiang-Bin, Shannon, Nic, Joynt, Robert

“…The quantum approximate optimization algorithm (QAOA) transforms a simple many-qubit wave function into one that encodes a solution to a difficult classical…”
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Learning quantum phases via single-qubit disentanglement by An, Zheng, Cao, Chenfeng, Xu, Cheng-Qian, Zhou, D. L.

“…Identifying phases of matter presents considerable challenges, particularly within the domain of quantum theory, where the complexity of ground states appears…”
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Simulating noisy quantum circuits with matrix product density operators by Cheng, Song, Cao, Chenfeng, Zhang, Chao, Liu, Yongxiang, Hou, Shi-Yao, Xu, Pengxiang, Zeng, Bei

“…Simulating quantum circuits with classical computers requires resources growing exponentially in terms of system size. Real quantum computer with noise,…”
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Detecting entanglement by pure bosonic extension by Zhu, Xuanran, Zhang, Chao, Cao, Chenfeng, Li, Youning, Poon, Yiu Tung, Zeng, Bei

“…In the realm of quantum information theory, the detection and quantification of quantum entanglement stand as paramount tasks. The relative entropy of…”
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Solution of SAT problems with the adaptive-bias quantum approximate optimization algorithm by Yu, Yunlong, Cao, Chenfeng, Wang, Xiang-Bin, Shannon, Nic, Joynt, Robert

“…The quantum approximate optimization algorithm (QAOA) is a promising method for solving certain classical combinatorial optimization problems on near-term…”
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SpinQ Gemini: a desktop quantum computing platform for education and research by Hou, Shi-Yao, Feng, Guanru, Wu, Zipeng, Zou, Hongyang, Shi, Wei, Zeng, Jinfeng, Cao, Chenfeng, Yu, Sheng, Sheng, Zikai, Rao, Xin, Ren, Bing, Lu, Dawei, Zou, Junting, Miao, Guoxing, Xiang, Jingen, Zeng, Bei

“…SpinQ Gemini is a commercial desktop quantum computing platform designed and manufactured by SpinQ Technology. It is an integrated hardware-software system…”
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Maximum Entropy Methods for Quantum State Compatibility Problems by Hou, Shi‐Yao, Wu, Zipeng, Zeng, Jinfeng, Cao, Ningping, Cao, Chenfeng, Li, Youning, Zeng, Bei

“…Abstract Inferring a quantum system from incomplete information is a common problem in many aspects of quantum information science and applications, where the…”
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Optical neural network quantum state tomography by Zuo, Ying, Cao, Chenfeng, Cao, Ningping, Lai, Xuanying, Zeng, Bei, Du, Shengwang

“…Quantum state tomography (QST) is a crucial ingredient for almost all aspects of experimental quantum information processing. As an analog of the “imaging”…”
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Noise-Assisted Quantum Autoencoder by Cao, Chenfeng, Wang, Xin

“…Phys. Rev. Applied 15, 054012 (2021) Quantum autoencoder is an efficient variational quantum algorithm for quantum data compression. However, previous quantum…”
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Accelerated variational quantum eigensolver with joint Bell measurement by Cao, Chenfeng, Yano, Hiroshi, Nakagawa, Yuya O

“…Phys. Rev. Research 6, 013205 (2024) The variational quantum eigensolver (VQE) stands as a prominent quantum-classical hybrid algorithm for near-term quantum…”
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Unveiling quantum phase transitions from traps in variational quantum algorithms by Cao, Chenfeng, Gambetta, Filippo Maria, Montanaro, Ashley, Santos, Raul A

“…Understanding quantum phase transitions in physical systems is fundamental to characterize their behaviour at small temperatures. Achieving this requires both…”
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Exploiting many-body localization for scalable variational quantum simulation by Cao, Chenfeng, Zhou, Yeqing, Tannu, Swamit, Shannon, Nic, Joynt, Robert

“…Variational quantum algorithms have emerged as a promising approach to achieving practical quantum advantages using near-term quantum devices. Despite their…”
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Quantum variational learning for quantum error-correcting codes by Cao, Chenfeng, Zhang, Chao, Wu, Zipeng, Grassl, Markus, Zeng, Bei

“…Quantum 6, 828 (2022) Quantum error correction is believed to be a necessity for large-scale fault-tolerant quantum computation. In the past two decades,…”
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Detecting Entanglement by Pure Bosonic Extension by Zhu, Xuanran, Zhang, Chao, Cao, Chenfeng, Li, Youning, Poon, Yiu Tung, Zeng, Bei

“…In the realm of quantum information theory, the detection and quantification of quantum entanglement stand as paramount tasks. The relative entropy of…”
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Learning quantum phases via single-qubit disentanglement by An, Zheng, Cao, Chenfeng, Xu, Cheng-Qian, Zhou, D. L

“…Quantum 8, 1421 (2024) Identifying phases of matter presents considerable challenges, particularly within the domain of quantum theory, where the complexity of…”
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