This paper presents a novel approach to proxy blind signatures in the realm of quantum circuits,aiming to enhance security while safeguarding sensitive information.The main objective of this research is to introduce a...This paper presents a novel approach to proxy blind signatures in the realm of quantum circuits,aiming to enhance security while safeguarding sensitive information.The main objective of this research is to introduce a quantum proxy blind signature(QPBS)protocol that utilizes quantum logical gates and quantum measurement techniques.The QPBS protocol is constructed by the initial phase,proximal blinding message phase,remote authorization and signature phase,remote validation,and de-blinding phase.This innovative design ensures a secure mechanism for signing documents without revealing the content to the proxy signer,providing practical security authentication in a quantum environment under the assumption that the CNOT gates are securely implemented.Unlike existing approaches,our proposed QPBS protocol eliminates the need for quantum entanglement preparation,thus simplifying the implementation process.To assess the effectiveness and robustness of the QPBS protocol,we conduct comprehensive simulation studies in both ideal and noisy quantum environments on the IBM quantum cloud platform.The results demonstrate the superior performance of the QPBS algorithm,highlighting its resilience against repudiation and forgeability,which are key security concerns in the realm of proxy blind signatures.Furthermore,we have established authentic security thresholds(82.102%)in the presence of real noise,thereby emphasizing the practicality of our proposed solution.展开更多
Quantum cloud platforms are of great importance for promoting near-term applications of quantum computing.Here we introduce the fully open-access Baihua platform,featuring 111 high-performance qubits with an average t...Quantum cloud platforms are of great importance for promoting near-term applications of quantum computing.Here we introduce the fully open-access Baihua platform,featuring 111 high-performance qubits with an average two-qubit control-Z gate fidelity exceeding 98%and a peak fidelity of 99.82%.The platform is implemented on a 156-qubit superconducting processor with heavy-hexagonal-like topology.Since its launch,Baihua has executed over two million quantum tasks,significantly promoting the research and development of hardware-efficient algorithms for practical quantum computing.展开更多
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
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
为探究多比特量子算法在量子芯片和模拟器中的实现现状,分别在IBM量子芯片和模拟器上运行Grover搜索算法、量子随机行走算法以及量子傅里叶变换算法。针对2 bit Grover搜索算法和2 bit量子随机行走算法,分析测量次数对运行结果的影响并...为探究多比特量子算法在量子芯片和模拟器中的实现现状,分别在IBM量子芯片和模拟器上运行Grover搜索算法、量子随机行走算法以及量子傅里叶变换算法。针对2 bit Grover搜索算法和2 bit量子随机行走算法,分析测量次数对运行结果的影响并选用最高可模拟次数对量子芯片和模拟器的运算结果进行比对。设计并运行5 bit量子傅里叶变换算法和3 bit Grover搜索算法,分别采用IBM Q模拟器进行最高次数的模拟。实验结果表明,量子芯片测试结果并没有随测量次数的增加而优化,模拟器计算结果的准确度明显优于量子芯片。展开更多
基金Project supported by the General Project of Natural Science Foundation of Hunan Province(Grant Nos.2024JJ5273 and 2023JJ50328)the Scientific Research Project of Education Department of Hunan Province(Grant Nos.22A0049 and 22B0699)。
文摘This paper presents a novel approach to proxy blind signatures in the realm of quantum circuits,aiming to enhance security while safeguarding sensitive information.The main objective of this research is to introduce a quantum proxy blind signature(QPBS)protocol that utilizes quantum logical gates and quantum measurement techniques.The QPBS protocol is constructed by the initial phase,proximal blinding message phase,remote authorization and signature phase,remote validation,and de-blinding phase.This innovative design ensures a secure mechanism for signing documents without revealing the content to the proxy signer,providing practical security authentication in a quantum environment under the assumption that the CNOT gates are securely implemented.Unlike existing approaches,our proposed QPBS protocol eliminates the need for quantum entanglement preparation,thus simplifying the implementation process.To assess the effectiveness and robustness of the QPBS protocol,we conduct comprehensive simulation studies in both ideal and noisy quantum environments on the IBM quantum cloud platform.The results demonstrate the superior performance of the QPBS algorithm,highlighting its resilience against repudiation and forgeability,which are key security concerns in the realm of proxy blind signatures.Furthermore,we have established authentic security thresholds(82.102%)in the presence of real noise,thereby emphasizing the practicality of our proposed solution.
基金the processor design and fabrication.The authors acknowledge support from the National Natural Science Foundation of China(Grant Nos.92365206,12404557)Innovation Program for Quantum Science and Technology(Grant Nos.2023ZD0300200,2021ZD0301802,2024ZD0301500).
文摘Quantum cloud platforms are of great importance for promoting near-term applications of quantum computing.Here we introduce the fully open-access Baihua platform,featuring 111 high-performance qubits with an average two-qubit control-Z gate fidelity exceeding 98%and a peak fidelity of 99.82%.The platform is implemented on a 156-qubit superconducting processor with heavy-hexagonal-like topology.Since its launch,Baihua has executed over two million quantum tasks,significantly promoting the research and development of hardware-efficient algorithms for practical quantum computing.
基金supported by the National Natural Science Foundation of China(Grant No.92365206)the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)+1 种基金supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘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.
基金supported by the Beijing Academy of Quantum Information Sciencessupported by the National Natural Science Foundation of China(Grant No.92365206)+2 种基金the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘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.
文摘为探究多比特量子算法在量子芯片和模拟器中的实现现状,分别在IBM量子芯片和模拟器上运行Grover搜索算法、量子随机行走算法以及量子傅里叶变换算法。针对2 bit Grover搜索算法和2 bit量子随机行走算法,分析测量次数对运行结果的影响并选用最高可模拟次数对量子芯片和模拟器的运算结果进行比对。设计并运行5 bit量子傅里叶变换算法和3 bit Grover搜索算法,分别采用IBM Q模拟器进行最高次数的模拟。实验结果表明,量子芯片测试结果并没有随测量次数的增加而优化,模拟器计算结果的准确度明显优于量子芯片。
