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Quafu-Qcover:Explore combinatorial optimization problems on cloud-based quantum computers
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作者 许宏泽 庄伟峰 +29 位作者 王正安 黄凯旋 时运豪 马卫国 李天铭 陈驰通 许凯 冯玉龙 刘培 陈墨 李尚书 杨智鹏 钱辰 靳羽欣 马运恒 肖骁 钱鹏 顾炎武 柴绪丹 普亚南 张翼鹏 魏世杰 增进峰 李行 龙桂鲁 金贻荣 于海峰 范桁 刘东 胡孟军 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第5期104-115,共12页
We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and c... We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers. 展开更多
关键词 quantum cloud platform combinatorial optimization problems quantum software
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Quafu-RL:The cloud quantum computers based quantum reinforcement learning
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作者 靳羽欣 许宏泽 +29 位作者 王正安 庄伟峰 黄凯旋 时运豪 马卫国 李天铭 陈驰通 许凯 冯玉龙 刘培 陈墨 李尚书 杨智鹏 钱辰 马运恒 肖骁 钱鹏 顾炎武 柴绪丹 普亚南 张翼鹏 魏世杰 曾进峰 李行 龙桂鲁 金贻荣 于海峰 范桁 刘东 胡孟军 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第5期29-34,共6页
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... 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. 展开更多
关键词 quantum cloud platform quantum reinforcement learning evolutionary quantum architecture search
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ScQ cloud quantum computation for generating Greenberger-Horne-Zeilinger states of up to 10 qubits
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作者 chi-tong chen Yun-Hao Shi +9 位作者 Zhongcheng Xiang Zheng-An Wang Tian-Ming Li Hao-Yu Sun Tian-Shen He Xiaohui Song Shiping Zhao Dongning Zheng Kai Xu Heng Fan 《Science China(Physics,Mechanics & Astronomy)》 SCIE EI CAS CSCD 2022年第11期84-89,共6页
In this study, we introduce an online public quantum computation platform, named as ScQ, based on a 1D array of a 10-qubit superconducting processor. Single-qubit rotation gates can be performed on each qubit. Control... In this study, we introduce an online public quantum computation platform, named as ScQ, based on a 1D array of a 10-qubit superconducting processor. Single-qubit rotation gates can be performed on each qubit. Controlled-NOT gates between nearest-neighbor sites on the 1D array of 10 qubits are available. We show the online preparation and verification of Greenberger-Horne-Zeilinger states of up to 10 qubits through this platform for all possible blocks of qubits in the chain. The graphical user interface and quantum assembly language methods are presented to achieve the above tasks, which rely on a parameter scanning feature implemented on ScQ. The performance of this quantum computation platform, such as fidelities of logic gates and details of the superconducting device, is presented. 展开更多
关键词 quantum computation quantum information quantum entanglement
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