What should be your first quantum programming language or library? Usually, people ask me about Qiskit and Cirq. Now of course there are other programming libraries and languages, and I’ve done some videos on them which you can check out on this channel, and I’ll be making more tutorials, so subscribe.
But here I’m going to cover some of the similarities, differences, pros and cons, and also go through some learning resources and how to find interesting papers and projects that you can do, for both Cirq and Qiskit, to help you decide which to learn.
Robert Loredo Learn Quantum Computing with Python and IBM Quantum Experience: A hands-on introduction to quantum computing and writing your own quantum programs with Python: https://amzn.to/3vZFQdV
Jack Hidary “Quantum Computing: An Applied Approach”: https://amzn.to/3scSJip
Cirq: https://quantumai.google/cirq
Tensorflow Quantum: https://www.tensorflow.org/quantum
Qiskit online textbook: https://qiskit.org/textbook/preface.html
Qiskit Slack: https://qiskit.slack.com
My Python Learning Resources Video: https://youtu.be/qdiBcIFrQlw
0:00 Quantum Coding!
0:34 What I Use
0:49 Similarities
1:43 Differences in Hardware Access
2:58 Quantum Algorithm Packages
3:09 Tensorflow Quantum
4:24 Qiskit Aqua
5:33 Resources for Qiskit
6:30 Cirq Learning
7:03 Projects
9:11 My Use Cases
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So first let’s start with the similarities of Qiskit and Cirq. They are both Python libraries, and they are both gate based languages.
And both are open source! So you can fork either one, and contribute to it publicly, or build upon it.
One major difference between the two is access to real quantum hardware.
Google right now does not provide access to their quantum hardware, while IBM does – they provide up to 15 qubit chips for public access, for free.
Now, should this stop you from using Cirq? It’s definitely cool to say you ran a circuit on a real quantum chip.
However, since public access is limited to 15 qubits, if you want to use more than 15 qubits, you’ll have to fall back to the simulator using Qiskit as well.
IBM’s general simulator can go up to 32 qubits (they have more specific simulator types with more qubits, but you’ll be using the general)
Google’s simulators go up to 30 qubits on the high performance external simulators. They also mention that the simulator can do more, but RAM doubles with each additional qubits, so I’m not sure that’s a hard 30 qubit limit.
Now, to no one’s surprise – one huge benefit Cirq has is Tensorflow Quantum and working on the quantum machine learning side.
TensorFlow Quantum focuses on quantum data and building hybrid quantum-classical models. It combines the quantum computing algorithms from Cirq, and mashes them with Traditional Tensorflow.
The tutorials cover Hello, Many Worlds, the traditional MNIST classification, and then moves into quantum convolutional neural networks. This quantum data tutorial is important, to build intuition for how you load and represent data when using quantum computing.
Qiskit has a package called Aqua that gives you use of algorithms for even above having to deal with it at the gate level. Aqua stands for Algorithms for QUantum Applications.
So it has some pre-built algorithms for:
Chemistry
Finance
Machine Learning and
Optimization
You can check out the tutorials and also look at the official documentation, because they provide a lot of examples for different projects done with Qiskit. The Optimization section covers problems like Traveling Salesman. With the finance section you can deep dive into pricing options, credit risk analysis, and portfolio optimization. You can also look at machine learning and chemistry applications.
So now that you have an idea of where to find resources for learning each library, Let’s talk about some cool papers you can implement. So again since these quantum computing libraries are gate based, you can try them on the other hardware. That may be a good exercise – take a paper written using Qiskit and implement it using Cirq, or vice versa!
Now, my first stop is always Arxiv.org, where there is a repository of papers. Just search for each of these platforms and you’ll find papers that use them!
Another quick tip for finding papers to replicate is to look up the researchers who work on these projects on Google scholar. So you can see what papers they’ve published using their own hardware and pick from there!
Don’t overanalyze, pick one and get started. The concept you learn about quantum with transfer, and the rest is SMOP (small matter of programming) and syntax.
Disclaimer: Affiliate links may be used in my recommendations! If you buy through my links I provide, I may receive a portion of the sale amount. This doesn’t change the price you pay.
#qiskit #cirq #quantumprogramming
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