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Qiskit — new modules

Time to do some quantum stuff

Introduction to Qiskit

IBM released in 2017 a framework to use their quantum computers and simulators. This library, named Qiskit (https://qiskit.org), is a framework written in python and built on top of the OpenQASM language (the machine language).

Initially, Qiskit consisted of four modules named:

• Terra: allows the user to work on the noisy quantum computer at the level of pulses, circuits, and algorithms. This module is the core component on which the rest of Qiskit is built.
• Aer: is the module to use and create performant simulators and representations of the current noisy devices. The module provides realistic noise to test quantum algorithms like a real device.
• Ignis: is the module to mitigate noise in a quantum circuit (application of filters). The module also provides tools to estimate the noise in the result and apply correction to minimize the effect of the noise. Ignis is applied on top of Aer and Terra.
• Aqua: is an acronym for Algorithms for QUantum computing Applications. It provides a long list of quantum algorithms and built-in elements to compose and construct new quantum algorithms.

Ignis and Aqua are currently deprecated. Recently, the IBM team released new modules specific to the applications and replaced the two previous modules to extend their methods.

• Optimization: it’s a module that contains all the quantum optimization algorithms (Quantum Approximate Optimization Algorithm, Variational Quantum Algorithm…). The package provides building blocks for quickly prototyping new quantum algorithms.
• Machine Learning: is a small module containing datasets and a few quantum machine learning algorithms (quantum support vectors, quantum generative adversarial networks, and variational quantum classifiers).
• Finance: contains datasets and building blocks to study stock problems and portfolio optimizations.
• Nature: is a module to study the behaviour of the atomistic energy levels. It provides building blocks to compute the ground state energy, molecule interaction, and excited states. The code includes chemistry drivers to calculate the molecular interaction.

How to install Qiskit?

If you go to the library’s website, the team provides the way to install Qiskit on different OS. I will assume that you know how to use command lines and have a python version installed. So, the easiest way is to use this command:

pip install qiskit

or

python -m pip install qiskit

Depending on the version of python and how it is installed, you can have pip3 instead of pip and python3 instead of python.

To install the optimization module, you need to use this command line:

pip install qiskit-optimization

To install the machine learning module, you need to use this command line:

pip install qiskit-machine-learning

To install the finance module, you need to use this command line:

pip install qiskit-finance

To install the nature module, you need to use this command line:

pip install qiskit-nature

How to learn Qiskit?

Qiskit is one of the most used quantum libraries on the market. Currently, the community is huge and has built lots of tutorials. But, IBM quantum also focuses on the education part. It’s their primary focus. The company wants to educate people to be ready when the market explodes.

The maintainers, developers and Qiskit advocates contribute to people's learning progress by providing a big online textbook” The beta version allows you to manipulate snippet of code and answer questions to test your understanding of the quantum world. You can find the textbook here: https://qiskit.org/learn

The authors provide tons of materials to start your learning journey (prerequisites). Quantum computing resources, like an introduction, multi-qubits and entanglement, states and qubits. These resources will help you go deeper with hardware and microwave pulses, quantum protocols, quantum algorithms and applications.

For the more hungry you also have access to quantum machine learning courses (based on the summer school given in 2021) and quantum games. The previous version of the summer school (2020) had a focus on hardware.

For those who prefer to watch videos I highly recommend the youtube channel of Qiskit providing documentaries, courses, seminars and more.

How to contribute?

You have different ways to be involved in the quantum community and in Qiskit:

• Qiskit is an open-source library you can contribute to the GitHub issues
• Participating in the Slack discussions
• Participating in IBM quantum challenges to solve real applications
• Participating in summer schools (2 weeks in July)
• Becoming an advocate
• Building tutorials or projects using Qiskit to share what you learn