Quantum Computing, Quantum Physics, Quantum Mechanics, Algorithms.
to minimize it by adjusting the parameters in small increments. The update step in gradient descent can be represented as follows: Here, x represents the parameters of the function, alpha is the learning rate (a small positive constant that controls the step size), and dJ(x) denotes the gradient of