A non-review of Quantum Machine Learning: trends and explorations

Dai-Long Ngo-Hoang

Quantum algorithms have evolved from initial proofs-of-concept to hundreds of specialized methods promising speed-ups in chemistry, cryptography, optimization, and machine learning. We conduct a Systematic Mapping Review (SMR) of 262 algorithms published between 2000 and April 2024, following PRISMA guidelines. Using searches across Scopus, IEEE Xplore, Web of Science, and arXiv, we screen 1,245 candidate papers and extract metadata for qualifying algorithms. We propose a taxonomy organizing algorithms by problem class and application domain. Bibliometric analyses show that simulation and variational optimization dominate, quantum machine learning (QML) exhibits the fastest growth, and gaps persist in quantum control, error correction, and domain-specific optimization. We discuss trends and recommend hardware-aware design and exploration of untapped problem structures.

Toran Verma, Botcha Kishore Kumar, Janga Rajendar +1 more

Dai-Long Ngo-Hoang

Quantum algorithms have evolved from early proofs-of-concept to hundreds of specialized methods promising computational speed-ups in fields such as chemistry, cryptography, optimization, and machine learning. This Systematic Mapping Review (SMR) analyzes 262 algorithms published between 2000 and April 2024, adhering to PRISMA guidelines. We searched Scopus, IEEE Xplore, Web of Science, and arXiv, screened 1,245 candidate papers, and extracted detailed metadata for qualifying algorithms. A two-dimensional taxonomy organizes algorithms by problem class and application domain. Bibliometric analyses reveal that simulation and variational optimization dominate, quantum machine learning (QML) shows the fastest growth, and significant gaps exist in quantum control, error correction, and domain-specific optimization. We discuss these trends and propose research directions emphasizing hardware-aware design and exploration of novel problem structures.

Marco Marcozzi, Ernestas Filatovas, Remigijius Paulavičius

Amani Lamine, Ameni Mejri, Bilel Charfi

Yanche Ari Kustiawan, Khairil Imran Ghauth

This research project presents a PRISMA-guided systematic literature review exploring the application of quantum machine learning (QML) in oncology, a rapidly growing field with fragmented evidence. The study synthesizes peer-reviewed studies from six databases (ACM, IEEE, MDPI, Scopus, Wiley, and PubMed) published between 2020 and 2026, screening 212 records and selecting 49 studies for analysis. The review examines clinical tasks, cancer domains, QML architectures, challenges, and future directions, revealing that current research mainly focuses on classification and detection, particularly in breast cancer and brain tumors, using hybrid quantum-classical models. Despite promising results, progress is limited by hardware constraints, simulator dependence, and insufficient clinical validation. The findings provide a clear overview of current QML trends and highlight future priorities, including real quantum hardware testing, diverse clinical datasets, standardized benchmarking, and stronger collaboration between computing and oncology communities.