Shruti Bamboria, Kiranbhai Ramabhai Dodiya, Kapil Kumar
This chapter examines the intersection of quantum computing, cybersecurity, and adversarial machine learning (AML), outlining both transformative opportunities and emerging risks. Quantum computing, leveraging superposition and entanglement, offers unparalleled computational speed-ups but simultaneously threatens classical cryptographic protocols via Shor's and Grover's algorithms. Meanwhile, AML reveals vulnerabilities in machine learning systems through evasion, poisoning, and inference attacks, posing risks in healthcare, finance, and autonomous domains. Quantum power could accelerate adversarial exploits yet also enhance defenses through anomaly detection, quantum key distribution, and hybrid quantum–classical security models. Persistent challenges—error correction, scalability, cost, and integration—limit deployment. The chapter underscores the urgency of adopting post-quantum cryptography, fortifying AML defenses, and promoting collaboration between quantum scientists and cybersecurity experts, concluding with a roadmap for building resilient, quantum-secure infrastructures.
Advances in computational intelligence and robotics book series•Mar 13, 2026•0 citations