Quantum-Enhanced Adversarial Robustness in Artificial Intelligence
Artificial Intelligence has achieved remarkable success across diverse application domains. However, its vulnerability to adversarial attacks poses significant challenges to reliability, security, and trustworthiness. Adversarial machine learning demonstrates that even highly accurate models can be manipulated through carefully crafted perturbations, raising serious concerns in safety critical systems such as healthcare, finance, and autonomous technologies. In parallel, quantum computing has emerged as a transformative paradigm capable of addressing complex computational problems through principles such as superposition, entanglement, and quantum interference. The convergence of these fi...
| Authors | Jaydip Sen |
|---|---|
| Primary category | cs.CR |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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