Most of classification and rule learning algorithms in machine learning use heuristic search to find part of rules for classification. Classification Associative Classification (AC) has shown a great dominance over many classification techniques. It integrates the rule discovery and classification process to build the classifier that supports decision making process. Artificial Immune Systems (AIS) have emerged during the last decade,It uses the population-based search model of evolutionary computation algorithms that it is regarded as a suitable way for dealing with complex search space. This paper proposes a Quantum-Negative Selection Algorithm (Q-NSA) for associative classification. It integrates quantum computing concepts and Negative Selection Algorithm (NSA) to building an efficient classifier by generating rule detectors to find the best subset of rules for all possible association rules. It employees a mutation operator with a quantum-based rotation gate to control and maintain diversity, and guides the search process. The performance of proposed algorithm is evaluated using benchmark datasets. The experimental results showed that the proposed algorithm is preformed well with large search space and has higher accuracy, and maintain diversity.