Qiskit_Global_Summer_School_2021

Labs

Labs completed are as a part of Qiskit Global Summer School (QGSS)-2021

Lab-1: Quantum Computing Operations and Algorithms

Lab-2: Variational Algorithms

Lab-3: Quantum Feature Maps, Kernels and Support Vector Machines

Lab-4: Training Quantum Circuits

Lab-5: Hardware Efficient Ansatze for Quantum Machine Learning

Lectures

Day 1

1.1: Vector Spaces, Tensor Products, and Qubits

1.2: Introduction to Quantum Circuits

Day 2

2.1: Simple Quantum Algorithms I

2.2: Simple Quantum Algorithms II

Day 3

3.1: Coherent Noise

3.2: Projection Noise, Measurement Noise, State Preparation Errors, Incoherent Errors

Day 4

4.1: Introduction to Classical Machine Learning

4.2: Advanced Classical Machine Learning

Day 5

5.1: Building a Quantum Classifier

5.2: Introduction to the Quantum Approximate Optimization Algorithm

Day 6

6.1: From Variational Classifiers to Linear Classifiers

6.2: Quantum Feature Spaces and Kernels

Day 7

7: Quantum Kernels in Practice

Day 8

8.1: Introduction and Applications of Quantum Models

8.2: Barren Plateaus, Trainability Issues, and How to Avoid Them

Day 9

9.1: Introduction to Quantum Hardware

9.2: Hardware Efficient Ansatz for QML

Day 10

10.1: Advandced QML Algorithms

10.2: Capacity and power of QML Models

All the lecture notes belongs to Qiskit Summer School-2021