Quantum Computing Threats and Encrypted Machine Learning

Abstract

As quantum computing advances towards practical implementation, its potential to disrupt current cryptographic protocols raises significant concerns for secure machine learning systems. This paper explores the intersection of quantum computing and encrypted machine learning, examining the vulnerabilities posed by quantum algorithms to traditional encryption methods. We discuss the implications for privacy-preserving machine learning techniques, emphasizing the need for quantum-resistant cryptographic solutions. Additionally, we explore current strategies and emerging technologies aimed at mitigating these threats, ensuring the future resilience of encrypted machine learning in a quantum-powered era.