Fidelity-Based Ant Colony Optimization for Control of Quantum System

The search time of traditional ant colony Algorithm (ACA) is too long, and it tends to trap in local optimizations. In this paper, a fidelity-based ACA with Q-learning is presented to solve above-mentioned problem and applied for control of quantum system. In the fidelity-based ACA with Q-learning, fidelity is used in heuristic function, which can help the system converge to the optimal solution. And the fidelity-based ACA with Q-learning can simplify the parameter setting of ACA algorithm. An example (a spin-1/2 system) is demonstrated to test the performance of the fidelity-based ACA with Q-learning. The results show that the fidelity-based ACA with Q-learning can efficiently avoid trapping in local optimal policies and increase the speed of convergence process.

[1]  Ian R. Petersen,et al.  Quantum control theory and applications: A survey , 2009, IET Control Theory & Applications.

[2]  Qiaoyu Yin,et al.  Continuous quantum ant colony optimization and its application to optimization and analysis of induction motor structure , 2010, 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA).

[3]  Thomas Stützle,et al.  Ant Colony Optimization Theory , 2004 .

[4]  Ling Wang,et al.  A novel quantum ant colony optimization algorithm and its application to fault diagnosis , 2008 .

[5]  Erlong Yang,et al.  Quantum ant colony optimization with application , 2010, 2010 Sixth International Conference on Natural Computation.

[6]  Gerard J. Milburn,et al.  Quantum Measurement and Control by Howard M. Wiseman , 2009 .

[7]  Akira Inoue,et al.  Optimal path planning for material and products transfer in steel works using ACO , 2011, The 2011 International Conference on Advanced Mechatronic Systems.

[8]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[9]  Christian Blum,et al.  Beam-ACO - hybridizing ant colony optimization with beam search: an application to open shop scheduling , 2005, Comput. Oper. Res..

[10]  Herschel Rabitz,et al.  Quantum control landscapes , 2007, 0710.0684.

[11]  Marco Dorigo.  Ant colony optimization , 2004, Scholarpedia.

[12]  Marco Dorigo,et al.  Ant colony optimization and its application to adaptive routing in telecommunication networks , 2004 .

[13]  Matthijs den Besten,et al.  An Ant Colony Optimization Application to the Single Machine Total Weighted Tardiness Problem , 2000 .

[14]  G. Reinelt.  The traveling salesman: computational solutions for TSP applications , 1994 .

[15]  Herschel A Rabitz,et al.  Quantum Optimally Controlled Transition Landscapes , 2004, Science.

[16]  Tzyh Jong Tarn,et al.  Quantum Reinforcement Learning , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  G. Milburn,et al.  Quantum Measurement and Control , 2009 .

[18]  I. Chuang,et al.  Quantum Computation and Quantum Information: Bibliography , 2010 .

[19]  Thomas Stützle,et al.  PLANTS: Application of Ant Colony Optimization to Structure-Based Drug Design , 2006, ANTS Workshop.

[20]  Thierry Paul,et al.  Quantum computation and quantum information , 2007, Mathematical Structures in Computer Science.

[21]  Zonghai Chen,et al.  QUANTUM COMPUTATION FOR ACTION SELECTION USING REINFORCEMENT LEARNING , 2006 .

[22]  Tzyh Jong Tarn,et al.  Fidelity-Based Probabilistic Q-Learning for Control of Quantum Systems , 2014, IEEE Transactions on Neural Networks and Learning Systems.