Qiskit

This is the Entire walkthrough of what I am following right from installation of Qiskit to some sample codes to implement. The repository reflects my understandings and work on Qiskit. Lets Start ✨

Introduction

Qiskit is an open source SDK for working with quantum computers at the level of pulses, circuits and algorithms. Main goal of Qiskit is to build a software stack that makes it easy for anyone to use quantum computers. Qiskit can be used to easily design experiments and run them on simulators and real quantum computers. Qiskit consists of four foundational elements:

• Qiskit Terra: Composing quantum programs at the level of circuits and pulses with the code foundation.

• Qiskit Aer: Accelerating development via simulators and noise models

• Qiskit Ignis: Addressing noise and errors

• Qiskit Aqua: Building algorithms and applications

Installations

I would be doing it on mainly on Google Colab and on my Ubuntu system too. It is recommended to use Python virtual environments to cleanly separate Qiskit from other applications for better experience. This issue is resolved for Google colab and as I am running Ubuntu on my VirtualBox, I would be directly installing the package there.

This is the only step for successful installation of qiskit in Ubuntu and Google Colab too.

• For Ubuntu

pip install qiskit

• For Colab

!pip install qiskit

Cross check whether Qiskit is installed correctly or not Run qiskit.__qiskit_version__ on Colab, the results should be look similiar as

{'qiskit': None,
 'qiskit-aer': '0.6.1',
 'qiskit-aqua': '0.7.5',
 'qiskit-ibmq-provider': '0.10.0',
 'qiskit-ignis': '0.4.0',
 'qiskit-terra': '0.15.2'}

We are good to go 👍

Quantum-Circuit-Optimization-with-PyZX

Now, we will optimize Quantum circuits using PyZX which is a Python tool implementing the theory of ZX-calculus for the creation, visualisation, and automated rewriting of large-scale quantum circuits. Demo can be seen here

For entire walkthrough visit the official repo here, I would be doing my work on Google Colab and further instructions in the Jupyter Notebook would follow the same. In the notebook a randomly generated curcuit is optimized, but optimizing such random circuit is of no use. It can be found inside Optimization_demo folder.

The Main goal would be to;

• Transform a given Quantum Circuit into one which is executable on a specific device, preserving measurement outcomes on a specific device. ( Compatibility )
• Find an implementation which takes maximal advantage of target device resources, while minimizing influence of decoherence and errors. ( Optimiation )

PyZX would play Hero in this part of the repo.