QML VS CML

Both Classical Machine Learning models and Quantum Machine Learning models have their own advantages. When compared Quantum Machine Learning is more advanced and with the right datasets and computing resources the results will be amazing. But sometimes due to some reasons the classical machine learning models outperform or perform equally with a quantum machine learning model.

Reasons:

- They are in an early stage of development. Prone to errors caused by noise, qubit decoherence, and gate imperfections. Due to these there may be some inaccuracies in the output.

- Limited number of qubits, if the complexity of the problem exceeds and the available qubits are less, then capturing of sufficient information will be hard.

- Encoding classical data into quantum states is non-trivial. The feature maps that convert classical data into quantum data may not always be efficient. This leads to loss of data.

- Quantum algorithms often use optimizers like COBYLA, which can struggle with high-dimensional and non-convex loss landscapes.

- Due to quantum hardware limitations, the quantum machine learning models are often tested on smaller datasets. Classical models perform well on smaller datasets but for quantum models, the datasets should be huge for more accuracy.

- Classical Machine Learning algorithms like logistic regression, decision trees, and neural networks have been extensively studied, tuned, and optimized over decades. In contrast, QML is still in its infancy, and many quantum algorithms are experimental.

- Google Colab's free tier is limited to 12GB of RAM and restricted computational resources, which can hinder performance and accuracy for resource-intensive machine learning tasks.

- PennyLane outperforms Qiskit in gradient-based optimizations and local simulations but may not match Qiskit’s hardware-level capabilities. For hybrid algorithms or projects involving classical ML frameworks, PennyLane is often preferred. If you have direct access to IBM quantum devices, Qiskit is the better choice.

Accuracies with different models and datasets:

Ransomware Dataset