A Survey on Quantum Machine Learning: Basics, Current Trends, Challenges, Opportunities, and the Road Ahead
We sincerely thank the editor and all reviewers for their detailed and constructive feedback. In response, we have made substantial revisions throughout the manuscript to improve its clarity, completeness, and scholarly rigor. Key algorithmic sections, including VQE and Quantum Kernels (Section 4), were expanded to include implementation choices, optimization challenges, and limitations such as barren plateaus, along with relevant mitigation strategies and foundational citations. Comparative summaries were added to Sections 4.4, 6.2, and 6.4 to highlight trade-offs between QML models, quantum hardware, and software tools. The quantum data section (Section 5) was reorganized to better distinguish classical preprocessing from quantum encoding, with expanded context on dataset use cases and challenges. Sections 2 and 3 were restructured to provide a clearer narrative of quantum principles and complexity theory, and Section 3.2 was revised to more accurately reflect the implications of quantum supremacy benchmarks. We have also clarified the current hardware constraints and resource limitations in Sections 2.6.2 and 4.8. Subsections 7.4–7.6 were revised to avoid speculative claims and emphasize concrete techniques. In Section 8, we now explicitly acknowledge the broader ethical and societal implications of QML and QC, including cryptographic risks and access disparities. These revisions collectively enhance the pedagogical value, technical depth, and relevance of our survey.