Cybersecurity faces a major challenge in the escalating sophistication of ransomware, which exploits vulnerabilities in critical systems and disrupts essential services across various sectors. Introducing an unprecedented approach, the Quantum-Layered Differential Analysis (QLDA) framework leverages quantum computing principles alongside differential pattern analysis to elevate ransomware detection to new standards of adaptability, accuracy, and speed. The QLDA framework is structured through a modular, multi-layered architecture, where quantum principles such as entanglement and superposition facilitate parallel data processing, allowing for the rapid detection of ransomware with minimized computational overhead. Experimental evaluations demonstrated QLDA's impressive detection accuracy of 97.8% and a notably low false-positive rate of 1.1%, outperforming traditional detection methods such as signaturebased and heuristic approaches. Furthermore, QLDA exhibited substantial scalability and efficiency, managing large datasets and adapting to previously unseen ransomware samples with a detection rate of 93.2%. These results affirm that QLDA offers a transformative enhancement in cybersecurity, providing a highly scalable and resilient method capable of identifying emerging ransomware threats in real-time, without reliance on pre-existing signature databases or heuristic rules. Through its novel integration of quantum computing and differential analysis, QLDA holds significant promise for advancing cybersecurity protections against ransomware within increasingly complex and dynamic digital ecosystems.