Two schemes for the implementation of the two-qubit Grover search algorithm in the ion trap system are proposed. These schemes might be experimentally realizable with presently available techniques. The experimental i...Two schemes for the implementation of the two-qubit Grover search algorithm in the ion trap system are proposed. These schemes might be experimentally realizable with presently available techniques. The experimental implementation of the schemes would be an important step toward more complex quantum computation in the ion trap system.展开更多
It is widely believed that Shor＇s factoring algorithm provides a driving force to boost the quantum computing research.However, a serious obstacle to its binary implementation is the large number of quantum gates. No...It is widely believed that Shor＇s factoring algorithm provides a driving force to boost the quantum computing research.However, a serious obstacle to its binary implementation is the large number of quantum gates. Non-binary quantum computing is an efficient way to reduce the required number of elemental gates. Here, we propose optimization schemes for Shor＇s algorithm implementation and take a ternary version for factorizing 21 as an example. The optimized factorization is achieved by a two-qutrit quantum circuit, which consists of only two single qutrit gates and one ternary controlled-NOT gate. This two-qutrit quantum circuit is then encoded into the nine lower vibrational states of an ion trapped in a weakly anharmonic potential. Optimal control theory（OCT） is employed to derive the manipulation electric field for transferring the encoded states. The ternary Shor＇s algorithm can be implemented in one single step. Numerical simulation results show that the accuracy of the state transformations is about 0.9919.展开更多
针对复杂U型障碍物环境中跳点搜索算法(jump point search,JPS)路径长、拐点多和人工势场法(artificial potential field,APF)陷入U型陷阱引起的路径曲折、寻路效率低等问题,提出融合改进JPS算法和APF算法(JPS^(*)-APF)的移动机器人路...针对复杂U型障碍物环境中跳点搜索算法(jump point search,JPS)路径长、拐点多和人工势场法(artificial potential field,APF)陷入U型陷阱引起的路径曲折、寻路效率低等问题,提出融合改进JPS算法和APF算法(JPS^(*)-APF)的移动机器人路径规划算法。首先,在传统JPS算法中增加角度偏差函数并删除冗余节点,减小搜索距离和转折次数;其次,改进JPS算法的拐点作为子目标点,分段引导APF算法逃出U型陷阱,自适应生成拐角障碍物斥力或动态子目标点提高路径平滑度;然后,在目标点区域添加对称虚拟障碍物解决目标不可达、融合外部斥力和重规划策略逃出局部最优,提高寻路效率;最后,适时加入相对速度斥力保证动态避障的安全性。针对不同U/L型障碍物环境进行数值仿真,结果表明,JPS^(*)-APF算法较IA^(*)-APF算法平均减少了51.5%的寻路时间和7.3%的路径长度,而且JPS^(*)-APF算法路径更平滑,能有效逃出U型陷阱并提升移动机器人的工作效率;同时通过真实环境实验测试验证了JPS^(*)-APF算法规划的可行性。展开更多
基金Project supported by Fok Ying Tung Education Foundation （Grant No 81008）, the National Natural Science Foundation of China （Grant Nos 60008003 and 10225421）, and Funds from Fuzhou University, China.
文摘Two schemes for the implementation of the two-qubit Grover search algorithm in the ion trap system are proposed. These schemes might be experimentally realizable with presently available techniques. The experimental implementation of the schemes would be an important step toward more complex quantum computation in the ion trap system.
基金supported by the National Natural Science Foundation of China(Grant No.61205108)the High Performance Computing(HPC)Foundation of National University of Defense Technology,China
文摘It is widely believed that Shor＇s factoring algorithm provides a driving force to boost the quantum computing research.However, a serious obstacle to its binary implementation is the large number of quantum gates. Non-binary quantum computing is an efficient way to reduce the required number of elemental gates. Here, we propose optimization schemes for Shor＇s algorithm implementation and take a ternary version for factorizing 21 as an example. The optimized factorization is achieved by a two-qutrit quantum circuit, which consists of only two single qutrit gates and one ternary controlled-NOT gate. This two-qutrit quantum circuit is then encoded into the nine lower vibrational states of an ion trapped in a weakly anharmonic potential. Optimal control theory（OCT） is employed to derive the manipulation electric field for transferring the encoded states. The ternary Shor＇s algorithm can be implemented in one single step. Numerical simulation results show that the accuracy of the state transformations is about 0.9919.
文摘针对复杂U型障碍物环境中跳点搜索算法(jump point search,JPS)路径长、拐点多和人工势场法(artificial potential field,APF)陷入U型陷阱引起的路径曲折、寻路效率低等问题,提出融合改进JPS算法和APF算法(JPS^(*)-APF)的移动机器人路径规划算法。首先,在传统JPS算法中增加角度偏差函数并删除冗余节点,减小搜索距离和转折次数;其次,改进JPS算法的拐点作为子目标点,分段引导APF算法逃出U型陷阱,自适应生成拐角障碍物斥力或动态子目标点提高路径平滑度;然后,在目标点区域添加对称虚拟障碍物解决目标不可达、融合外部斥力和重规划策略逃出局部最优,提高寻路效率;最后,适时加入相对速度斥力保证动态避障的安全性。针对不同U/L型障碍物环境进行数值仿真,结果表明,JPS^(*)-APF算法较IA^(*)-APF算法平均减少了51.5%的寻路时间和7.3%的路径长度,而且JPS^(*)-APF算法路径更平滑,能有效逃出U型陷阱并提升移动机器人的工作效率;同时通过真实环境实验测试验证了JPS^(*)-APF算法规划的可行性。
