Quafu-Qcover:Explore combinatorial optimization problems on cloud-based quantum computers

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.

Quafu-RL:The cloud quantum computers based quantum reinforcement learning

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.

三种镍基合金显微组织及性能的对比研究

对三种镍基合金材料Inconel 625、Inconel 625plus及Inconel 718的元素含量、显微组织及性能进行对比研究。结果表明:Inconel 625合金的主要沉淀强化相为少量γ'(Ni3(Al、Ti)),Inconel 625plus合金的主要沉淀强化相为大量γ'(Ni3(Ti)),而Inconel 718合金的主要沉淀强化相为体心四方结构的γ"相(Ni3Nb),同时辅以面心立方结构的弱强化相γ'(Ni3(Al、Ti));Inconel 625,Inconel 625plus和Inconel 718室温下屈服强度分别为450MPa,900MPa和1 050MPa,同时其具有良好的塑性和韧性数据。在工程应用中较高温度下,Inconel 625plus及Inconel 718合金的屈服强度分别为815MPa和785MPa,同时具有良好的塑性和韧性指标。

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capability for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of taskdecomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV’s control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV’s flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.

Delayed-measurement one-way quantum computing on cloud quantum computer

One-way quantum computation focuses on initially generating an entangled cluster state followed by a sequence of measurements with classical communication of their individual outcomes.Recently,a delayed-measurement approach has been applied to replace classical communication of individual measurement outcomes.In this work,by considering the delayed-measurement approach,we demonstrate a modified one-way CNOT gate using the on-cloud superconducting quantum computing platform:Quafu.The modified protocol for one-way quantum computing requires only three qubits rather than the four used in the standard protocol.Since this modified cluster state decreases the number of physical qubits required to implement one-way computation,both the scalability and complexity of the computing process are improved.Compared to previous work,this modified one-way CNOT gate is superior to the standard one in both fidelity and resource requirements.We have also numerically compared the behavior of standard and modified methods in large-scale one-way quantum computing.Our results suggest that in a noisy intermediate-scale quantum(NISQ)era,the modified method shows a significant advantage for one-way quantum computation.