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A course on Quantum Machine Learning

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

Getting Started (Notes and Coding tutorials)

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

Optimization

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

Quantum Machine Learning

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

Quantum Machine Learning Tutorials and Worked Examples

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.

Quantum Machine Learning with Qiskit

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