Quantum Machine Learning ( QML)

Why QML?

In classical machine learning, increasing the number of parameters leads to challenges in measuring both combined and individual contributions of features. The curse of dimensionality makes data representation and optimisation extremely difficult. Quantum Machine Learning (QML) provides a way to represent and process such high-dimensional data more efficiently by exploiting superposition, entanglement, and quantum parallelism.

QML architectures share structural similarities with classical neural networks, typically consisting of:

• Input Layer
• Processing Layer
• Output Layer
• Optimizer

Input Layer: Quantum Embedding

The role of the input layer is to convert classical data into a quantum representation, a process known as quantum embedding. Popular embedding methods include binary encoding, amplitude encoding, and angle encoding, and the choice depends on the nature of the dataset.

For example, in angle encoding, classical data values are mapped as rotation angles on the Bloch sphere. Quantum gates such as Rx(θ),Ry(θ), and Rz(θ) perform rotations around the respective axes, effectively embedding the input into quantum states.