Designing Feature Maps in Quantum Neural Networks

Quantum Neural Networks (QNNs) represent a promising intersection of quantum computing and machine learning, aiming to exploit quantum principles for enhanced data processing and pattern recognition. At their core, QNNs extend classical neural network architectures into quantum Hilbert spaces, where information is encoded into quantum states and manipulated through parameterized quantum circuits. A critical component in this process is the quantum feature map, which embeds classical data into high-dimensional quantum states. Feature maps allow QNNs to harness properties such as superposition and entanglement, enabling richer data representations and the potential for more powerful decision boundaries than classical models. This poster provides an overview of the basic principles of QNNs, explains the role of quantum feature maps in data encoding, shows how to design a quantum feature map with specific examples, and highlights the importance of feature maps in bridging the gap between classical inputs and quantum models.