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SPECIAL TOPIC — Quantum computing and quantum sensing
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Quafu-RL: The cloud quantum computers based quantum reinforcement learning
Yu-Xin Jin(靳羽欣), Hong-Ze Xu(许宏泽), Zheng-An Wang(王正安), Wei-Feng Zhuang(庄伟峰), Kai-Xuan Huang(黄凯旋), Yun-Hao Shi(时运豪), Wei-Guo Ma(马卫国), Tian-Ming Li(李天铭), Chi-Tong Chen(陈驰通), Kai Xu(许凯), Yu-Long Feng(冯玉龙), Pei Liu(刘培), Mo Chen(陈墨), Shang-Shu Li(李尚书), Zhi-Peng Yang(杨智鹏), Chen Qian(钱辰), Yun-Heng Ma(马运恒), Xiao Xiao(肖骁), Peng Qian(钱鹏), Yanwu Gu(顾炎武), Xu-Dan Chai(柴绪丹), Ya-Nan Pu(普亚南), Yi-Peng Zhang(张翼鹏), Shi-Jie Wei(魏世杰), Jin-Feng Zeng(曾进峰), Hang Li(李行), Gui-Lu Long(龙桂鲁), Yirong Jin(金贻荣), Haifeng Yu(于海峰), Heng Fan(范桁), Dong E. Liu(刘东), and Meng-Jun Hu(胡孟军)
Chin. Phys. B, 2024, 33 (
5
): 050301. DOI:
10.1088/1674-1056/ad3061
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133
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With the rapid advancement of quantum computing, hybrid quantum-classical machine learning has shown numerous potential applications at the current stage, with expectations of being achievable in the noisy intermediate-scale quantum (NISQ) era. Quantum reinforcement learning, as an indispensable study, has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts. However, despite the progress of quantum processors and the emergence of quantum computing clouds, implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits (PQCs) on NISQ devices remains infrequent. In this work, we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud. The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases. Moreover, we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices. We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.
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A family of quantum von Neumann architecture
Dong-Sheng Wang(王东升)
Chin. Phys. B, 2024, 33 (
8
): 080302. DOI:
10.1088/1674-1056/ad50be
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40
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We develop universal quantum computing models that form a family of quantum von Neumann architectures, with modular units of memory, control, CPU, and internet, besides input and output. This family contains three generations characterized by dynamical quantum resource theory, and it also circumvents no-go theorems on quantum programming and control. Besides universality, such a family satisfies other desirable engineering requirements on system and algorithm design, such as modularity and programmability, hence serves as a unique approach to building universal quantum computers.
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Nonlinear time-reversal interferometry with arbitrary quadratic collective-spin interaction
Zhiyao Hu(胡知遥), Qixian Li(李其贤), Xuanchen Zhang(张轩晨), He-Bin Zhang(张贺宾), Long-Gang Huang(黄龙刚), and Yong-Chun Liu(刘永椿)
Chin. Phys. B, 2024, 33 (
8
): 080601. DOI:
10.1088/1674-1056/ad4ff7
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Atomic nonlinear interferometry has wide applications in quantum metrology and quantum information science. Here we propose a nonlinear time-reversal interferometry scheme with high robustness and metrological gain based on the spin squeezing generated by arbitrary quadratic collective-spin interaction, which could be described by the Lipkin-Meshkov-Glick (LMG) model. We optimize the squeezing process, encoding process, and anti-squeezing process, finding that the two particular cases of the LMG model, one-axis twisting and two-axis twisting outperform in robustness and precision, respectively. Moreover, we propose a Floquet driving method to realize equivalent time reverse in the atomic system, which leads to high performance in precision, robustness, and operability. Our study sets a benchmark for achieving high precision and high robustness in atomic nonlinear interferometry.