We at Quantum Zeitgeist love the Julia language and we think you will too. We liken it to the natural successor to Python. It does a great deal of what python does today but natively. Julia is also a great language for quantum computing simulations because its inbuilt libraries can handle Linear Algebra with total ease. We have written about Julia before and some of the advantages the language can offer.
Whilst the Python language seems to be the language of choice these days for just about everything, it is not always the best tool for the job. There is an increasing role for new language such as Julia which can help researchers and developers with the quest towards quantum computing. There are libraries such as Yao which enables Differentiable Quantum Programming In Julia. Such libraries are crucial in the development of variational algorithms used in topics such as machine learning. Yao can make use of native GPU programming in Julia and specialisation based on multiple dispatch, also achieving state-of-the-art performance on intermediate-sized quantum circuits.
The new features of Julia 1.6 are given in the latest release.
Check out our previous article on whether Julia could replace python and be transformation in Quantum Computing. We have also highlighted some basics of the Language Julia with a ‘101’ Julia tutorial and we have also briefly written about how would be learners can get access to learning Julia.
