Efficient cross-modal retrieval involves searching similar items across different modalities, e.g., using an image(text) to search for texts(images). To speed up cross-modal retrieval, hashing-based methods threshold continuous embeddings into binary codes, inducing substantial loss of accuracy retrieval. To further improve retrieval performance, several quantization-based methods quantize embeddings into real-valued codewords to maximumlly preserve inter-modal and intra-modal similarity relation, while the discrimination between dissimilar data is ignored. To address these challenges, we propose, for the first time, a novel discriminate cross-modal quantization(DCMQ) which nonlinearly maps different modalities into a common space where ir-relevant data points are semantically separable: the points belonging to a class lie in a cluster that is not overlapped with other clusters corresponding to other classes. An effective optimization algorithm is developed for the proposed method to jointly learn the modality-specific mapping functions, the sharing codebooks, the unified binary codes and a linear classifier. Experimental comparison with state-of-the-art algorithms over three benchmark datasets demonstrates that DCMQ achieves significant improvement in search accuracy.