Quantum Machine Learning - 2021 Qiskit Global Summer School

Explore quantum machine learning through this comprehensive two-week intensive summer school featuring twenty lectures, five hands-on lab exercises, mentorship sessions, and live Q&A discussions. Begin with fundamental quantum computing concepts including vector spaces, tensor products, qubits, and quantum circuits before progressing to simple quantum algorithms and understanding noise in quantum systems. Dive into classical machine learning foundations and advanced techniques, then transition to quantum applications by building quantum classifiers and exploring the Quantum Approximate Optimization Algorithm. Master variational algorithms through practical implementation, learn to develop quantum feature spaces and kernels, and gain hands-on experience with quantum support vector machines. Examine quantum model applications while addressing critical challenges like barren plateaus and trainability issues in quantum circuit training. Discover hardware-efficient ansatze for quantum machine learning and explore their practical implementation on real quantum hardware. Conclude with advanced quantum machine learning algorithms, analysis of quantum model capacity and power, and insights into the future direction of the field, all designed to empower quantum researchers and developers with practical skills for independent quantum application exploration.

Syllabus

Lecture 1.1 - Vector Spaces, Tensor Products, and Qubits
Lecture 1.2 - Introduction to Quantum Circuits
Lecture 2.1 - Simple Quantum Algorithms I
Lecture 2.2 - Simple Quantum Algorithms II
Lecture 3.1 - Noise in Quantum Computers - part 1
Lecture 3.2 - Noise in Quantum Computers - part 2
Lab 1 - Introduction to Quantum Computing Algorithms and Operations
Lecture 4.1 - Introduction to Classical Machine Learning (ML)
Lecture 4.2 - Advanced Classical Machine Learning (ML)
Lecture 5.1 - Building a Quantum Classifier
Lecture 5.2 - Introduction to the Quantum Approximate Optimization Algorithm and Applications
Lab 2 - Introduction to Variational Algorithms
Lecture 6.1 - From Variational Classifiers to Linear Classifiers
Lecture 6.2 - Quantum Feature Spaces and Kernels
Lecture 7.1 - Quantum Kernels in Practice
Lab 3 - Introduction to Quantum Kernels and Support Vector Machines
Lecture 8.1 - Introduction and Applications of Quantum Models
Lecture 8.2 - Barren Plateaus, Trainability Issues, and How to Avoid Them
Lab 4 - Introduction to Training Quantum Circuits
Lecture 9.1 - Introduction to Quantum Hardware
Lecture 9.2 - Hardware Efficient Ansatze for Quantum Machine Learning
Lab 5 - Introduction to Hardware Efficient Ansatze for Quantum Machine Learning
Lecture 10.1 - Advanced QML Algorithms
Lecture 10.2 - The Capacity and Power of Quantum Machine Learning Models
The Future of Quantum Machine Learning