Abstract: Based on the immune clonal selection theory and the novel genetic quantum algorithm, an Immune Clonal Quantum Algorithm (ICQA) is proposed to solve high complexity of optimum multiuser detection in code division multiple access systems. Using this algorithm, the vaccine based on Hopfield neural network is inoculated into the Clonal Quantum Algorithm (CQA ) to improve further the fitness of the population at each generation. Such a hybridization of the CQA with the stochastic Hopfield neural network reduces its computational complexity by providing faster convergence. In addition, a better initial data estimation supplied by the CQA improves the performance of the vaccine, and the inoculated vaccine improves the performance of the CQA. The uniform theoretic framework of the making vaccine based on the stochastic Hopfield neural network is presented. Simulation results show that the proposed detector not only can achieves the global optimization value in fast convergence rate, but also is obviously superior to the conventional detector and the multiuser detectors based on previous intelligent algorithms in cancellation of the multiple access interference and the near-far effect.