A systematic review of quantum machine learning: algorithms, application domains, and emerging trends

Quantum Machine Learning (QML) integrates the principles of quantum mechanics superposition, entanglement, and interference into mathematical and logical operations that classical computers cannot emulate. Despite the limitations of today’s noisy intermediate-scale quantum (NISQ-ERA) devices, these platforms already sustain small- to medium-scale machine-learning workloads, catalyzing rapid expansion in QML research. Guided by PRISMA methodology, we systematically screened 5,463 publications published between January 2015 and June 2025 and identified 160 high-quality studies that implemented or evaluated QML algorithms on real or simulated quantum processors. The selected literature converges into four thematic categories: (i) quantum adaptations of classical algorithms, (ii) variational and hybrid quantum–classical models, (iii) quantum linear-algebra accelerators, and (iv) quantum-inspired generative and reinforcement techniques. Application domains span image and signal classification, cybersecurity, finance, chemistry, and healthcare, with benchmarks such as MNIST, CIC-IDS2019 and credit-risk corpora featuring prominently in the reviewed corpus. Separately from the reviewed literature, Sect. 6 reports our own five-seed benchmark of five quantum and two classical classifiers on five public tabular datasets (Breast Cancer, Diabetes, Iris, Sonar and Bank Marketing). Comparative analyses reveal that hybrid QML systems typically match or marginally exceed the performance of state-of-the-art classical baselines while employing fewer parameters. Nevertheless, a clear, reproducible quantum advantage remains elusive, hindered by data-encoding overheads, circuit-depth constraints, environmental noise, and barren-plateau training phenomena. By integrating algorithmic trends, empirical outcomes, and hardware considerations, this review delineates QML’s present capabilities and identifies critical technological milestones error mitigation, scalable qubit architectures, and efficient data-loading schemes needed to unlock its full potential.