Learn Quantum Computing

Ian Hellström | 7 September 2022 | 5 min read

No idea where to begin with quantum computing? Here is a curated list of articles, books, videos, courses, and online programmes to get ready for the revolution!


Good introductory articles are Quantum Algorithm Implementations for Beginners (2022) by the Los Alamos National Laboratory and Basic Quantum Algorithms (2022). Once you are comfortable with the basics of quantum circuits, I recommend Equivalent Quantum Circuits (2011) to understand quantum circuit decompositions.

Surface Codes: Towards Practical Large-Scale Quantum Computation (2012) and Quantum Error Correction for Beginners (2013) are suitable introductions to quantum error correction. This is needed to build fault-tolerant quantum computers and benefit from NISQ-era hardware.

An Introduction to Quantum Machine Learning (2014) and Quantum Machine Learning (2018) are for those who are interested in quantum computers for machine learning applications. An Introduction to Quantum Machine Learning for Engineers (2022) also includes a substantial introduction to quantum computing that is not too technical. Afterwards, you can check out and understand Pennylane’s QML blog.


The following books are my personal favourites for quantum computing:

  1. A First Introduction to Quantum Computing and Information (2018) by Bernard Zygelman.
  2. Quantum Computing: An Applied Approach (2019) by Jack Hidary, which comes with plenty of colour images and problem sets.
  3. Mathematics of Quantum Computing (2019) by Wolfgang Scherer, which is best for those who prefer a rigorous mathematical treatment. It comes with a 250+ page appendix in case you need to brush up on any prerequisite mathematics.
  4. Quantum Computation and Quantum Information (2010) by Michael Nielsen and Isaac Chuang is a classic.
  5. Machine Learning with Quantum Computers (2021) by Maria Schuld and Francesco Petruccione explains the basics of machine learning and quantum computing before merging both fields.
  6. Machine Learning Meets Quantum Physics (2020) is mostly geared towards ML for simulations in quantum chemistry for materials discovery.

If you speak German and prefer a text in German, I can recommend Quantum Computing Verstehen (2008) by Matthias Homeister.

While not a book per se, the Qiskit textbook is excellent to learn IBM’s Qiskit and quantum computing in a more hands-on approach.

There are many more books published on the topic of quantum computing. Quantum Computing since Democritus (2013) by Scott Aaronson is not really a textbook on quantum computing, as it deals mostly with philosophy and whatever topic quantum computing can be linked to. At least, the book's introduction is honest about that fact.

Quantum Computing for Everyone (2020) tries, in my view, too hard to come up with analogies, such as a 'quantum clock' to represent spin and measurement. That may be suitable for certain people, but as a physicist it irks me more than it aids, especially since the book lacks both rigour and practicality.

There are a few books published by Apress, Packt, Manning, and Pragmatic, although I tend to avoid these, as their books have disappointed me too often. Their publications may generally be more focused on practitioners, but the material is more current and often covered better in official tutorials.


The Linux Foundation and World Bank offer Fundamentals of Quantum Computing, which is a free, short, high-level introduction to the field of quantum computing, suitable for anyone. Understanding Quantum Computing is somewhat similar, although it requires a subscription to LinkedIn Learning. Both video series are very high level, so perhaps best as very first introductions or for executives, not necessarily practitioners.

While not open to registrations any longer, the Womanium Quantum Computing programme has made their many lectures and lab walk-throughs publicly available for free.

There are also lectures from Stanford University’s Quantum Computing Bootcamp, CERN, Andrew Childs, David Mermin (Cornell), John Preskill (Caltech), Peter Wittek (Toronto) with a forked repository on QML, and UPM.

Interactive programmes

IBM’s Qiskit Summer School happens once a year, which includes lectures and labs. It is more advanced than the introductory, self-paced Quantum Explorers programme, also by IBM. The latter is loosely based on the Qiskit textbook. At the end of these courses, you are able to develop and optimize basic quantum circuits with Qiskit, mitigate errors, and perform basic physics and chemistry simulations on quantum simulators and actual quantum computers in IBM’s environment. Both are entirely free.

If you prefer Q# to Qiskit and Python, check out Brilliant’s Quantum Computing course, although most of it does not rely on any particular language or library.

Q-CTRL has an interactive learning environment to learn quantum computing, too. It is perhaps best for people with nearly no experience, as otherwise many of the interactive components may feel too banal.

There is also a free interactive course Quantum Computing for the Very Curious by Andy Matuschak and Michael Nielsen. The interactivity is limited to its spaced-repetition cards, which may not be for everyone, although they can be skipped. In that case, the ‘course’ becomes an online text.

Online courses

QWorld offers beginner workshops several times a year. You can check out the materials in public repositories for some of these courses.

TU Delft’s Quantum 101: Quantum Computing and Quantum Internet (edX) is available for free and as a paid verified certificate ($298). In the latter case, you have more exercises, which I can recommend. It is ideal for people who are interested in the low-level details of the hardware, although it presupposes knowledge of quantum mechanics, condensed matter physics, and semiconductors.

Oxford University’s Quantum Computing (£895) is not suitable for beginners, although in two days the lecturer provides a lot of insight. A Qiskit course prior to Oxford’s online course is perhaps a good idea.

There is also a MOOC edX for Quantum Machine Learning by the University of Toronto. It offers a free audit track and a verified option.

Beyond that, there are several more expensive options:


Quantum Inspire’s knowledge base is a decent resource for beginners to look up concepts.

Do you want to get your hands dirty? Microsoft’s Quantum Katas offer a way to learn Q# by yourself. Another option is Teach Me Quantum, which is for Qiskit.

If you are already well on your way in your quantum computing journey, the quantum algorithm zoo is great to explore quantum algorithms with their (theoretical) speed-ups compared to classical algorithms.

And for those who want to pursue a degree, Quantum Insider has a list of options.

What’s next?

In another post, I review various online learning opportunities for quantum computing in depth. That way, you can make an informed decision on what is best for you. Have fun exploring the quirky world of quantum computing!