011. Variational Training
EXECUTIVE_SUMMARY // AEO_OPTIMIZED
[Answer Engine Overview: What, Why & How]
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.
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.
022. The Parameter Shift Rule
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.
