This article concludes a three part series introducing you to quantum computing and qiskit. The first part, here, explained the basics required to understand the quantum teleportation algorithm. The second part, here, then looked at how the quantum teleportation algorithm works. This final part looks at how the algorithm can be implemented using Qiskit, IBM’s quantum software Python SDK. It is therefore a prerequisit to this that you understand how the teleportation circuit we are trying to implement (below) works.

The first thing we need to do is import all the modules we are going to be using. If you…

The aim of this article is to understand how the quantum teleportation algorithm works. It assumes basic knowledge of quantum computing, in particular X, Z, H and CNOT gates, as well as Bell states and how to create them. If any of this seems unfamiliar see the first part in this series here that covers all of the basics. The final part, here, covers how to run the algorithm on a real IBM quantum device using their SDK, Qiskit.

You may be familiar with the word teleportation from pop culture such as the ‘beam me up’ technology from Star Trek…

This post gives a beginner their first look into quantum computing. It covers all the basics required to understand how quantum teleportation works. For the teleportation algorithm itself see the second part of this series here, and for how it is implemented using Qiskit on a real quantum computer, see the third part here. For this tutorial I will assume a basic understanding of linear algebra and braket notation. Although I will use braket notation throughout, all of this could also be though of in terms of normal matrix notation too.

As I am sure you are all familiar with…

For our entry into the QHack Quantum Machine Learning Hackathon our team, named QLords, decided to look into the use of quantum generative adversial networks (qGANs), QAOA and VQE for portfolio analysis.

Our code is available in this repository.

In 1952, Markovitz proposed a novel theory that exploits diversification to engineer a portfolio with higher returns for lower risk, going against conventional beliefs that risk has a positive linear relationship to reward. Mean variance portfolio optimization is an analysis technique that was born from Markovitz’s work. What Markovitz added to the game was the consideration of variance and covariance between…