The SenSIP Center conducts a broad range of research and workforce development activities in quantum machine learning (QML), focusing on the integration of quantum computing with signal processing, sensing, imaging, and data analytics. Current efforts include QML methods for solar energy monitoring and fault detection, medical image analysis, speech and audio processing, radar and SAR image classification, anomaly detection, and generative learning systems. Research projects investigate hybrid classical–quantum architectures using platforms such as IBM Qiskit and Amazon Braket, while also exploring educational innovations through interactive software tools and web-based laboratories. SenSIP’s QML activities are closely integrated with NSF-supported REU, RET, and international research programs, enabling undergraduate students, graduate students, and educators to gain hands-on experience with quantum algorithms, simulations, and emerging applications of quantum-enhanced.