Wolfram Language Paclet Repository
Community-contributed installable additions to the Wolfram Language
Example Repository Functions
In this Tech Note, we document the implementation and utilization of essential functions used in the for Quantum Computing algorithms. The examples include the Quantum Natural Gradient Descent, Stochastic Parameter Shift Rule and more.
By providing a comprehensive overview and usage guidelines for these functions, we aim to introduce new and experienced users into quantum optimization techniques, quantum machine learning and quantum computing research.
Gradient-Based Optimization Methods
Gradient-based optimization methods represent a cornerstone in the field of numerical optimization, offering powerful techniques to minimize or maximize objective functions in various domains, ranging from machine learning and deep learning to physics and engineering. These methods leverage the gradient, or derivative, of the objective function with respect to its parameters to iteratively update them in a direction that reduces the function's value.
In this Tech Note, we'll provide illustrative examples for both conventional gradient descent and quantum natural gradient descent methods.
GradientDescent[f,{ value 1 value 2 | calculates the gradient descent of f using value i |
QuantumNaturalGradientDescent[f,{ value 1 value 2 | calculates the gradient descent of f using value i metric |
We will briefly demonstrate how to apply all these functions in Wolfram quantum framework.
GradientDescent
GradientDescent
QuantumNaturalGradientDescent
QuantumNaturalGradientDescent
Examples Custom Functions
Feel free to explore the examples we've prepared on quantum computing within the . This section exclusively focuses on functions tailored for each examples and their specific application.
Quantum Natural Gradient Descent
Quantum Natural Gradient Descent
Stochastic Parameter Shift–Rule
Stochastic Parameter Shift–Rule
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