Table 1 Summary of research in the field of classical and quantum ML.
Ref | Dataset | Techniques | Result | XAI | Noise | Feature selection | Year |
|---|---|---|---|---|---|---|---|
RoEduNet-SIMARGL2021, CICIDS-2017 | RF, ADA, DNN, SVM, KNN, MLP, LIGHT GBM | 99% | Yes | No | Yes | 2024 | |
Audio sensor data | SVM, RF, LR, GNB, EGB | 80.28% | Yes | No | Yes | 2024 | |
Case Western Reserve University (CWRU) bearing dataset | Convolutional Long Short-Term Memory (CLSTM) | n/a | Yes | No | Yes | 2024 | |
ShipsEar dataset and simulated submarine data | RF, ADA boost, GBDT, X boost | 94.5%,76%,95%,96.7% | No | No | Yes | 2023 | |
Phase classification dataset | QSVM, VQC | 97.73,96.49 | Yes | No | Yes | 2025 | |
MNIST FMNIST, KMNIST, and CIFAR10 | Quantum autoencoder + VQC | 65% | Yes | No | No | 2025 | |
MNIST, Ionosphere, waveform, Madelon, synth_10, synth_100 | QUBO | 90, 78,87 | No | No | Yes | 2025 | |
Wisconsin breast cancer data, kaggles’s Club data | QUBO | 69,62, 72.06 | No | No | Yes | 2023 |
