Quantum AI vs. Classical AI: A Comparative Analysis

Neelesh Kumar; Rashmi Gupta

doi:10.55524/ijirem.2025.12.6.8

Abstract

Classical Artificial Intelligence (AI), particularly deep learning, has become a transformative agent in many fields. However, as data complexity grows, classical hardware faces significant challenges with inherently exponential problems. Quantum AI (QAI), or Quantum Machine Learning (QML), emerges as a new computational paradigm leveraging quantum mechanics to tackle this complexity. This paper presents a comprehensive comparative analysis of classical and quantum AI. We compare their foundational computational principles, data representation methods, core algorithmic strengths, and most suitable problem domains. The analysis highlights that while state-of-the-art classical models like Long Short-Term Memory (LSTM) networks remain dominant for high-accuracy regression tasks (achieving very low error metrics), quantum models such as Variational Quantum Classifiers (VQC) demonstrate significant promise for complex classification and pattern recognition tasks (achieving promising accuracy). We further discuss the practical limitations within the current Noisy Intermediate-Scale Quantum (NISQ) era and conclude that the most pragmatic path forward lies in hybrid quantum-classical systems, where each paradigm is used to augment the other's capabilities.

Keywords

Quantum AI, Classical AI, Quantum Machine Learning (QML), Comparative Analysis, NISQ, LSTM, Variational Quantum Classifier (VQC), Computational Complexity.

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Cites this article as

N. Kumar, R. Gupta, "Quantum AI vs. Classical AI: A Comparative Analysis", International Journal of Innovative Research in Engineering and Management (IJIREM), Vol-12, Issue-6, Page No-43-49, 2025. Available from: https://doi.org/10.55524/ijirem.2025.12.6.8