←←Back to Homepage

This course will take you through the basic theory required to understand quantum machine learning.

Here you can discover the basic tools needed to use PennyLane through simple demonstrations. Learn about training a circuit to rotate a qubit, machine learning tools to optimize quantum circuits, and introductory examples of photonic quantum computing.

Sr. No	Title	Description	Notebook	Medium
1.	What is Quantum Machine Learning	Reading Material related to QML and background
2.	Basic Qubit Rotation	Wish to implement the rotation quantum circuit:
3.	Quantum Gradient and Backpropagation	Theory related to the Parameter-Shift rule
4.	Quantum Gradient and Backpropagation	Tutorial related to the Parameter-Shift rule
5.	Adjoint Differentiation	Adjoint differentiation straddles two strategies, taking benefits from each.
6.	Gaussian Transformation	Basic principles of continuous variable (CV) photonic devices.
7.	Plugins and Hybrid Computation	Introduces the notion of hybrid computation by combining several PennyLane plugins.
8.	Noisy Circuits	Learn how to simulate noisy circuits using built-in functionality in PennyLane
9.	Penny Lane + AWS braket	Computing gradients with Pennylane and AWS Braket

Here you will find demonstrations showcasing quantum optimization. Explore various topics and ideas, such as the shots-frugal Rosalin optimizer, the variational quantum thermalizer, or barren plateaus in quantum neural networks.

Sr. No	Title	Description	Notebook	Medium
1.	Introduction to QAOA	The applications of QAOA are broad and far-reaching, the performance of the algorithm is of great interest to the quantum computing research community

Sr. No	Title	Description	Notebook	Medium
1.	Quantum models as Fourier series	This demonstration is based on the paper The effect of data encoding on the expressive power of variational quantum machine learning models

Check the repository with full details regarding some of the worked examples.

Sr. No	Title	Description	Notebook	Medium
1.	Quantum Variational Classifier	Using variational approach to classify Iris dataset
2.	Data Re-Uploading Classifier	Making a quantum classifier by only using single qubit
3.	Galaxy Detection using QML	Developing galaxy detection technique from the telescope image via QML.
4.	QCD Equation of State Classification using QSVM	Developing a Quantum Support Vector Machine model for Quantum Chromodynamics equation of state.

Sr.No	Title	Description	Notebook	Medium
1	Mathematical Introduction	Introduction to mathematical concepts used in quantum computing
2	Introduction to Qiskit	Overview of the Qiskit framework and its components
3	Classical and Quantum Probability Distribution	Comparison of classical and quantum probability distributions, including the Bloch sphere
4	Measurement and Mixed states	Understanding quantum measurement and mixed states
5	Evolution in closed and open systems	Dynamics of quantum systems in closed and open systems
6	Classical and Quantum Many body physics	Study of many-body systems from classical and quantum perspectives, including entanglement
7	Gate model quantum computing	Implementation of quantum algorithms using gate operations and circuit models
8	Adiabatic quantum computing	Introduction to adiabatic quantum computing, including its physical principles and algorithms
9	Variational circuits	Overview of variational circuits and their applications
10	Sampling a thermal state	Explanation of thermal states in quantum systems and sampling techniques
11	Discrete optimization and ensemble learning	Application of quantum computing to discrete optimization and ensemble learning problems
12	Kernel methods	Introduction to kernel methods and their application in quantum computing
13	Training a probabilistic model	Explanation of probabilistic models and how to train them using quantum computing techniques
14	Quantum phase estimation	Quantum algorithm for estimating the eigenvalues of a unitary operator
15	Quantum matrix inversion	Introduction to quantum matrix inversion and its applications