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[1] Quantum Private Array Content Comparison Based on Multi-Qubit Swap Test[J]. Mathematics. 2025, 13(11): 3422. https://doi.org/10.3390/math13233827. ÖпÆÔºÈýÇø£¬Í¨ÐÅ×÷Õß

[2] »ùÓÚ¿çģ̬עÒâÁ¦»úÖƺÍÈõ¼à¶½Ê½¶Ô±ÈѧϰµÄÐé¼ÙÐÂÎżì²âÄ£ÐÍ[J]. ÐÅÏ¢°²È«Ñо¿, 2025, 11(8) : 693-701. CSCDÆÚ¿¯/T2ÆÚ¿¯£¬Í¨ÐÅ×÷Õß

[3] ÈںϴóÓïÑÔÄ£ÐͺÍÖ¤¾Ý³éÈ¡µÄÊÂʵºË²éÄ£ÐÍ[J]. Î人´óѧѧ±¨(Àíѧ°æ) .?2025 ,71 (04): 485-494. CSCDÆÚ¿¯/T2ÆÚ¿¯£¬Í¨Ñ¶×÷Õß

[4] Privacy-Preserving and Poisoning-Defending Federated Learning in Fog Computing [J]. IEEE Internet of Things Journal. 2024, 11(3): 5063-5077. ÖпÆÔºÒ»Çø£¬Í¨ÐÅ×÷Õß

[5] New Quantum Private Comparison Using Four-Particle Cluster State[J]. Entropy. 2024, 26, 512. https://doi.org/10.3390/e26060512. ÖпÆÔºÈýÇø£¬Í¨ÐÅ×÷Õß

[6] Quantum metric learning with fuzzy-informed learning[J]. Physica A: Statistical Mechanics and its Applications 643 (2024) 129801. https://doi.org/10.1016/j.physa.2024.129801. ÖпÆÔº¶þÇø£¬Í¨ÐÅ×÷Õß

[7] Adversarial examples detection based on quantum fuzzy convolution neural network[J]. Quantum Information Processing (2024) 23:143, https://doi.org/10.1007/s11128-024-04310-3. ÖпÆÔºÈýÇø£¬Í¨ÐÅ×÷Õß

[8] Á¿×ÓÄ£ºýÐÅÏ¢¹ÜÀíÊýѧģÐÍÑо¿[J]. µç×ӿƼ¼´óѧѧ±¨, 2024, 53 (02): 284-290. EIÖÐÎÄÆÚ¿¯/T1ÆÚ¿¯£¬µÚ1×÷ÕߺÍͨÐÅ×÷Õß

[9] A backdoor attack against quantum neural networks with limited information[J]. Chin. Phys. B, 2023, 32 (10): 100306. ÖпÆÔºÈýÇø£¬Í¨ÐÅ×÷Õß

[10]Enhancing adversarial robustness of quantum neural networks by adding noise layers[J]. New J. Phys. 25 (2023) 083019. ÖпÆÔº¶þÇø£¬Í¨ÐÅ×÷Õß

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