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Quantum computing is one of the new fields with a lot of promises to change the world's industries in terms of problem solutions. High-speed problem solutions are some of the promises quantum computers have compared to classical computers. In turn, the field requires more skilled professionals who can program quantum computers. In this article, get to explore the top 10 quantum programming languages one has to learn in 2024 and know the liabilities associated with it.
Qiskit is an open-source high-level quantum programming language developed by IBM that allows building, simulating, and running quantum circuits on IBM's quantum computing systems. Qiskit is developed in Python and provides a full set of tools in quantum computing.
It is extremely versatile and also very widely used, in academia and industry. Qiskit is very well documented and has an enthusiastic community with several educational resources put forward for an easy start by beginners.
Price: Free and open-source
Learning Resources: There are free courses on Qiskit from IBM via their Qiskit Textbook and Qiskit Documentation.
Q# is a quantum programming language by Microsoft as part of its Quantum Development Kit; it is meant to interact with classical languages, including C# and Python. It helps to make a solution that is also a quantum-classical hybrid development. Algorithms and Libraries aligned with Q#, provides a broad set of quantum algorithms and libraries.
Microsoft's ecosystem integrates nicely with Azure Quantum in the arena of cloud-based quantum computing, and, thus, it will be very fitting for all those operating in that ecosystem. The language is also supported by Visual Studio, which is familiar to a large number of developers.
Price: Free and open-source.
Learning Resources: The Q# Documentation page features a lot of documentation, tutorials, and even a quantum Katas learning platform provided for free by Microsoft.
Cirq is an open-source quantum programming framework, initiated by Google, for writing, simulating, and running quantum circuits to be used on Google quantum processors. Cirq is special in that it is used for implementing and optimizing quantum circuits on near-term quantum computers.
Price: Free and open-source
Learning Resources: The Cirq Documentation and Cirq Tutorials are available online without any cost
Developed by Rigetti Computing, Forest is a quantum programming language that operates with PyQuil (a Python library for quantum programming). Forest allows users to build and run quantum circuits through Rigetti’s quantum processors and simulators.
Rigetti is a major player in the field of quantum computing. As you learn Forest, you get access to the Rigetti Quantum Cloud, Rigetti Forest, and their novel Quil instruction set.
Price: Free and open-source.
Learning Resources: Both Forest Documentation and PyQuil Documentation can be easily accessed online without any charges.
ProjectQ is an open-source quantum programming framework for implementing quantum algorithms in Python and running them on multiple backends like simulators and quantum hardware. It is highly modular so it can be extended with other quantum computing tools.
ProjectQ is designed to be used at ease along with flexibility; great for beginner and advanced users. Its modularity allows for experimentation and the development of custom quantum gates and circuits.
Price: Free and open-source
Learning Resources: ProjectQ Documentation and tutorials are freely accessible
Quipper is a quantum programming language developed by a team at the University of Oxford, designed for quantum algorithm implementation, and supporting a range of quantum operations. Quipper is particularly well suited for conducting large-scale quantum computations.
The first thing to perceive about Quipper is its flexibility and scalability. These qualities together with the advanced quantum algorithms make it also highly practical. It has very strong theoretical foundations while keeping a good balance for academic research.
Price: it is free and open source.
Learning Resources: A quite full guide to language in the Quipper Documentation.
Although this language is among the very first programming languages in quantum computing, it has been designed to express quantum algorithms. It uses a syntax that is at a very high level and greatly resembles the syntax of classical programming languages, hence more feasible for classical programmers.
The simplicity and cleanliness of the syntax make QCL very accessible to beginners in quantum computing. It is, again, majorly useful for educational and teaching purposes and also in the early stages of quantum algorithm development.
Price: Free, open source
Learning Resources: Official QCL Documentation has detailed instructions and examples.
OpenQASM is the Quantum Assembly Language and it is, hence, IBM's low-level quantum programming language. It expresses quantum circuits at a hardware abstraction level. It is intrinsic to how Qiskit operates. OpenQASM offers a hardware-agnostic framework for the representation of quantum circuits.
In the development and optimization of quantum hardware, OpenQASM is central to any person's work. OpenQASM has to be known to those who want to work with quantum gates and circuits directly on IBM's quantum hardware.
Price: Free and open source
Learning Resources: OpenQASM Specifications and tutorials are online for free.
Strawberry Fields is a full-stack quantum programming library developed by Xanadu for photonic quantum computing. It includes a Python library for the simulation and execution of quantum circuits on photonics-based quantum processors.
Xanadu's focus on photonic quantum computing is a different approach to the same problem, and knowledge of Strawberry Fields brings one closer to this emerging technology. This language is highly accessible and fundamentally biased towards quantum machine learning applications.
Price: Free, open source
Learning Resources: Strawberry Fields Documentation has detailed guides and examples.
Yao.jl is a quantum computing framework developed in Julia, a high-performance language for quantum circuit simulation and the development of quantum algorithms. Yao.jl offers an extensively optimized environment for quantum programming that brings out the strengths of Julia in numerical computation.
Yao.jl would be very fit for those doing high-performance simulations who are familiar with the Julia programming language. Being part of the Julia ecosystem has made Yao.jl quite instrumental in current research into quantum algorithms.
Price: Free, Open Source
Learning Resources: Tutorials and step-by-step guides in Yao.jl's Documentation
The science of quantum computing is experiencing significant growth, leading to the emergence of new languages and frameworks that cater to the demands of this innovative sector. The aforementioned languages are the greatest choices for users of all skill levels. Anyone interested in learning about quantum programming can access the majority of these languages because they are free and open-source. Acquiring knowledge of one or more of these languages in 2024 will place you at the forefront of quantum computing, regardless of your role as a researcher, developer, or enthusiast.
