Learn about Quantum Layers in this comprehensive AI & Artificial Intelligence tutorial. The future of deep learning.
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A QNN is essentially a function f(x, w) that maps input data x to an output using trainable parameters w. This is identical to classical deep learning but executed on a quantum state.
Since we cannot 'look inside' a quantum computer without collapsing the state, we use the parameter shift rule to calculate gradients by shifting the weights and measuring the difference.
Pascual Vila
Frontend Instructor // Code Syllabus
A parameterized circuit structure.
// Ansatz contextA method for computing gradients on hardware.
// Parameter Shift contextZero-gradient regions in the parameter landscape.
// Barren Plateau contextQuantum Entanglement And Gates
Quantum Generative Adversarial Networks
Quantum Support Vector Machines QSVM
Qubits And Superposition
Capstone Building A Quantum Classifier
Challenges In Quantum Hardware