README.md

Qiskit Global Summer School 2021 Labs

Labwork at Qiskit Global Summer school 2021 demonstrating applied algorithms and simulations on Quantum Computing and Quantum Machine Learning using Qiskit.

Lab 1: Introduction to Qiskit

Basic Rotations on One Qubit and Measurements on the Bloch Sphere

Quantum Circuits Using Multi-Qubit Gates

Oracles and the Deutsch-Jozsa algorithm

Deutsch-Jozsa Problem

Quantum Solution to the Deutsch-Josza Problem

Lab 2: Variational Quantum Algorithms

Variational Quantum Eigensolvers

Variational quantum eigensolvers use the variational method to approximate the ground state and minimal eigenvalue of a Hamiltonian $H$. The trial state now corresponds to a quantum state prepared by a variational quantum circuit and the corresponding expectation value is measured by executing the circuit on a quantum computer. A classical optimizer is then used to tune the circuit parameters and minimize the measured expectation value.

Lab 3: Quantum Kernels and Support Vector Machines

Data Encoding

Quantum Feature Maps

Quantum Kernel Estimation

Quantum Support Vector Classification

Lab 4: Training Parameterized Quantum Circuits

Finite difference gradients

Analytic gradients

Quantum Natural Gradient

Simultaneous Perturbation Stochastic Approximation

Building a variational Quantum classifier

Exponentially vanishing gradients (Barren plateaus)

Lab 5: Quantum Process Tomography

Only Shot Noise

T1/T2 Noise Model