Assessing The Reality of Quantum Advantage in Machine Learning

Quantum computing has generated significant expectations for its potential to enhance machine learning (ML) tasks, with theoretical research proposing computational advantages ranging from polynomial to exponential speedups for specific subroutines.However, a critical disconnect remains: theoretical or asymptotic speedup claims do not automatically imply a practical, end-to-end advantage.Current evidence does not clearly establish whether these claimed advantages translate into measurable superiority over optimised classical approaches.This review critically assesses the relationship between theoretical claims of quantum advantage and the currently available practical and experimental evidence.We critically examine the evidence and assumptions underlying claimed speedups, including the overheads associated with data encoding and access, as well as the use of appropriate and competitive classical baselines for fair comparison.The scope of this assessment covers key limiting factors, such as scalability, trainability, hardware noise in the noisy intermediate-scale quantum (NISQ) era, and overall resource requirements.By distinguishing between theoretical potential and demonstrated practical performance, this work identifies where current evidence is convincing and where it remains insufficient.The review identifies the specific conditions required for the credible validation of quantum advantage and argues that future progress must be positioned around rigorous, realistic, and end-to-end evaluation rather than theoretical complexity claims alone.