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Quantum Genetic Algorithm and its application to multi-user detection

Fei Li, Liang Hong, Baoyu Zheng

Published Oct 1, 2008• 10 citations
Publisher

Abstract

Quantum genetic algorithm (QGA) is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Instead of binary, numeric, or symbolic representation, QGA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator to drive the individuals toward better solutions. A novel multi-user detector based on QGA is proposed. An analysis is given to the theoretical basis and practical performance of the proposed detector. The simulation results show that the proposed detector has more powerful properties in bit error rate than conventional genetic algorithm based detector, namely a 2-3 dB gain in performance can be achieved. The results demonstrated the effectiveness and the applicability of QGA in multi-user detection.

DOI
10.1109/icosp.2008.4697525

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Quantum genetic algorithm (QGA) is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Instead of binary, numeric, or symbolic representation, QGA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator to drive the individuals toward better solutions. A novel multi-user detector based on QGA is proposed. An analysis is given to the theoretical basis and practical performance of the proposed detector. The simulation results show that the proposed detector has more powerful properties in bit error rate than conventional genetic algorithm based detector, namely a 2-3 dB gain in performance can be achieved. The results demonstrated the effectiveness and the applicability of QGA in multi-user detection.
The optimal solution for the conventional multi-user detection is a NP hard problem.Taking advantages of the properties of quantum mechanics such as quantum parallelism and entanglement can solve this problem efficiently.A multi-user detection based on quantum genetic algorithm is proposed,which integrates genetic algorithm with quantum computing theory.The computer simulation results show that the method has more powerful properties than the detector based on classical genetic algorithms and has powerful properties in bit error rate.
The genetic algorithm is an efficient optimization tool for its independence of problems, intrinsic parallelism and inherent learning capacity; however, it has some disadvantages such as slow convergence and prematurity. In this paper, a novel algorithm, called the quantum genetic algorithm—QGA, is proposed based on the combination of the quantum theory with the evolutionary theory. By adopting the qubit chromosome as a representation, QGA can represent a linear superposition of solutions due to its probabilistic representation. The random observation simulating the quantum collapse can bring diverse individuals, and the evolution of quantum chromosome can also pilot the evolution. So it has better diversity than the classical genetic algorithm. Rapid convergence and good global search capacity characterize the performance of QGA. The paper not only proves the global convergence of the QGA, but also some simulation experiments are shown to prove its superiority over other algorithms.
This paper proposes a novel evolutionary computing method called a genetic quantum algorithm (GQA). GQA is based on the concept and principles of quantum computing such as qubits and superposition of states. Instead of binary, numeric, or symbolic representation, by adopting qubit chromosome as a representation GQA can represent a linear superposition of solutions due to its probabilistic representation. As genetic operators, quantum gates are employed for the search of the best solution. Rapid convergence and good global search capability characterize the performance of GQA. The effectiveness and the applicability of GQA are demonstrated by experimental results on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that GQA is superior to other genetic algorithms using penalty functions, repair methods and decoders.
Multi-user detection based on quantum optimization algorithm has aroused intense interests recently. Quantum clone genetic algorithm has many shortcomings such as: low efficiency, poor population diversity, slow convergence speed, easy to trap in local minimums, blindness in global optimal searching direction and so on. An improved quantum clone genetic algorithm (IQCGA) is proposed in this paper. The algorithm have many merits: (1) Adopting nicking probability division initialization strategy, the population diversity is enhanced; (2) Introducing quantum whole interference crossover, the information is spread in the whole population, so it helps to avoid trapping in local minimums, and accelerates convergence speed; (3) Making use of adaptation strategy in quantum rotate gate updating, the searching speed for optimal solution is accelerated; (4) In order to avoid premature and evolution stagnation, the superior individual whole crossover quantum catastrophe strategy is adopted, which helps the population to search the objective solution in different directions. Simulation results prove that the multiuser detection based on given algorithm has lower bit error, rapider converge speed, better resisting MAI ability and better Near-far Resistance ability than other multi-user detections based on quantum clone genetic algorithm and classical genetic algorithms obviously.
Quantum algorithm (QA) with updating of quantum gates and catastrophe of population is compared with quantum genetic algorithm (QGA) by including crossover and mutation for quantum bits. Furthermore, a framework of hybrid quantum GA is proposed which combines the quantum based search and classic genetic search, and hybrid QGA with binary encoding (BQGA) and hybrid QGA with real encoding (RQGA) are presented. Numerical simulation on typical problems show that the performances of RQGA are the best among all testing algorithms and it is much robust on parameters and initial conditions.
This paper proposes a new parallel evolutionary algorithm called parallel quantum-inspired genetic algorithm (PQGA). Quantum-inspired genetic algorithm (QGA) is based on the concept and principles of quantum computing such as qubits and superposition of states. Instead of binary, numeric, or symbolic representation, by adopting the qubit chromosome as a representation, QGA can represent a linear superposition of solutions due to its probabilistic representation. QGA is suitable for parallel structures because of rapid convergence and good global search capability. That is, QGA is able to possess the two characteristics of exploration and exploitation simultaneously. The effectiveness and the applicability of PQGA are demonstrated by experimental results on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that PQGA is superior to QGA as well as other conventional genetic algorithms.