Quantum Software: A Guide to Leading Platforms and Programming Languages

Quantum computers, quantum technologies, and quantum programming are no longer concepts of science fiction — they are now part of our present-day technological reality. 

Quantum computing operates on principles of quantum mechanics, enabling qubits to exist in states of superposition and entanglement. These properties allow quantum computers to process multiple possibilities simultaneously, unlike classical computers that process information sequentially. 

Our previous article explored Quantum-as-a-Service in depth. 

In the context of software development, this means quantum computing can handle tasks that would take classical systems years — or even centuries — to complete. For developers, this opens new opportunities to design software capable of addressing previously unapproachable problems.

What Is Quantum Software Development?

Quantum software development involves creating algorithms and applications designed to run on quantum processors (QPUs). These programs leverage the mechanics of quantum systems to solve complex problems that classical computers struggle to handle.

Instead of binary bits, quantum software uses quantum logic gates, circuit models, and measurement rules to manipulate qubits. For example, a Hadamard gate (H) can place a qubit into superposition, and a CNOT gate can entangle two qubits—basic building blocks for powerful quantum algorithms like Grover’s (search) or Shor’s (factoring). Developers often work in high-level quantum languages that abstract the underlying quantum math while enabling practical implementations.

Quantum Programming Languages

It’s worth noting that Python is one of the fastest ways to dive into the world of quantum computing. Originally developed over thirty years ago by the Python Software Foundation, Python is a beginner-friendly programming language known for its simplicity and readability. Many quantum frameworks have been specifically designed with Python in mind, making it an ideal starting point for aspiring quantum developers.

Several software development kits (SDKs) and platforms have emerged to support quantum programming:

• Qiskit (IBM Quantum): Python-based, supports quantum circuit design, simulation, and real hardware execution.
• Cirq (Google): Focuses on near-term quantum algorithms and hardware-specific control.
• PennyLane (Xanadu): Integrates quantum computing with machine learning workflows.
• Q# (Microsoft): A domain-specific language for scalable quantum algorithm design.
• Ocean SDK (D-Wave): Designed for quantum annealing and optimization problems.

These are relatively new programming languages ​​and frameworks that simplify the development of quantum algorithms. Currently, the number of people trained in quantum programming languages ​​is small compared to those who know classical programming languages. This creates a demand for quantum programming specialists and opens up new opportunities for software companies.

Types of Quantum Computing Languages

The number of quantum computing languages is growing all the time. Based on an open-source mentality of sharing knowledge and resources, quantum programming languages have been designed to assist quantum algorithms using high-level constructs. 

Some companies like Microsoft, IBM, Google and Rigetti, are developing open-source development kits. They provide the tools necessary for software developers to solve their own problems and enable them to access to simulators or quantum computers to implement their quantum algorithms through the cloud.

A quantum simulator is an implementation of quantum gates by using classical gates. For example, Intel provides a Quantum Simulator that can simulate general quantum gates in order to test their software in simulation.

These kits often allow the use of classic programming languages, such as Python, or quantum software languages, such as Q# developed by Microsoft.

Here are some examples of quantum development kits (by Cem Dilmegani):

• D-Wave-Ocean
• Rigetti – Forest
• IBM – Qiskit
• Google – Cirq
• Microsoft – Quantum Development Kit (QDK)
• Zapata – Orquestra
• 1QBit – 1QBit SDK
• Amazon – Braket SDK
• ETH Zurich – ProjectQ
• Xanadu – Strawberry Fields
• Riverlane – Anian

Top 5 Quantum Programming Languages

1. Qiskit

Qiskit is an open-source quantum computing framework created by IBM. It’s written in Python and offers an approachable way to create and run quantum circuits. It supports simulators and also allows users to run code on real IBM quantum processors via the cloud.

It provides an easy-to-use interface for designing and running quantum circuits, along with tools for simulating quantum systems and optimizing quantum algorithms. As a widely adopted tool, Qiskit is one of the best quantum programming languages for beginners and experienced developers alike.

Qiskit is backed by extensive documentation, a strong community, and an official learning platform Qiskit Textbook. 

2. Cirq 

Cirq, developed by Google Quantum AI, is another popular quantum programming library. It allows developers to create, edit, and run quantum circuits on both simulators and real quantum hardware. It is especially suitable for tasks that require fine control over qubits and operations. Cirq is used in Google’s own quantum research, including the Sycamore processor and experiments involving quantum supremacy.

Cirq’s user-friendly framework enables the creation of intricate quantum circuits, giving direct control over quantum operations. It also addresses the challenges of quantum computing, such as noise and errors, by providing tools for simulation and error correction.

In essence, Google Cirq bridges the gap between classical and quantum computing, inviting developers to shape the future of computation.

3. Q#

Q# is a high-level, open-source programming language developed by Microsoft for writing quantum programs. Q# makes it easy for developers to write, test, and debug quantum algorithms on both classical and quantum hardware.

Q# is a stand-alone language offering a high level of abstraction. There is no notion of a quantum state or a circuit. Instead, Q# implements programs in terms of statements and expressions, much like classical programming languages.

Q# can be used to write quantum algorithms, simulate them on classical computers, and then run them on actual quantum computers. Q# also provides a library of built-in quantum operations, such as quantum gates and measurements, that are used to manipulate qubits and perform quantum computations.

4. Ocean™

Ocean™ software is a suite of open-source Python tools accessible via the Ocean Software Development Kit on both the D-Wave GitHub repository and within the Leap quantum cloud service. D-Wave, a pioneer in the quantum computing industry, designed Ocean to allow developers to experiment with and leverage the power of D-Wave’s Advantage quantum computer to solve complex problems.

The Ocean™ software development kit (SDK) is the D-Wave™ suite of open-source Python tools for solving hard problems with quantum computers.

For quantum computing, as for classical, solving a problem requires that it be formulated in a way the computer and its software understand. Ocean tools help you take a problem for a particular application (social network analysis, for instance, which is an example of an optimization problem) and transform it to a form that is solvable on a quantum computer. This form, called the binary quadratic model (BQM), allows the quantum computer to minimize a quadratic polynomial over a set of binary variables.

5. PennyLane

PennyLane by Xanadu is an open-source software framework for quantum machine learning, quantum chemistry, and quantum computing, with the ability to run on all hardware. 

It is a useful tool for quantum machine learning and quantum computing. It involves creating circuits with adjustable parts, measuring how well they perform using a scorecard (cost function), and then fine-tuning the circuits to improve their performance. PennyLane integrates seamlessly with the Python ecosystem.

Classical computations,optimization or training of models, are executed using the standard scientific computing or machine learning libraries such as SciPy in Python. PennyLane provides an interface to these libraries and integrates these with quantum simulators to provide an interface between classical and quantum computing.

+BONUS 

PyQuil

PyQuil is a Python library for quantum programming using Quil, the quantum instruction language developed at Rigetti Computing. The source is hosted on github. Quantum computing system provider Rigetti Computing — which is working on both quantum hardware circuits as well as the software layers, allows to start using  quantum machine learning even without having personal access to quantum computing hardware.

PyQuil serves three main functions:

• Easily generating Quil programs from quantum gates and classical operations
• Compiling and simulating Quil programs using the Quil Compiler (quilc) and the Quantum Virtual Machine (QVM)
• Executing Quil programs on real quantum processors (QPUs) using Quantum Cloud Services (QCS).

What is the best programming language for quantum computing?

There’s no single “best” programming language for quantum computing. The best programming language for quantum computing depends on the use case and hardware compatibility. 

Python is the most popular & beginner-friendly language. Python is widely used in quantum computing thanks to its simplicity and the strong ecosystem of quantum libraries. Used in Qiskit (IBM), PennyLane (Xanadu), Cirq (Google), Braket SDK (Amazon). 

Q# is created specifically for quantum computing by Microsoft. Other notable quantum computing programming languages include Silq (high-level quantum programming), Quipper (Haskell-based), and OpenQASM (an assembly-like quantum language). 

Julia is another popular coding language used in quantum computing. It is particularly useful for quantum computing because it can handle complex numerical computations quickly and accurately. C++ is also pretty commonly used in quantum computing. It is used in many quantum computing frameworks, including Qiskit and Cirq. It’s also used in a number of quantum computing libraries, such as the Quantum Toolkit and the Quantum Computing Toolkit.

If you’re just getting started, Python is the best entry point due to its widespread support and learning resources. Most major quantum SDKs are Python-based, and it allows you to quickly experiment with quantum circuits using simulators or cloud-based QaaS platforms.

Conclusions

The main of the key benefits of Quantum Software Development is its potential to dramatically accelerate processing speeds and increase computational efficiency. Tasks that would take classical supercomputers years to complete could potentially be solved in hours or even minutes on a quantum computer. This opens enormous opportunities for industries ranging from finance and pharmaceuticals to logistics and cybersecurity.

The growing ecosystem of quantum programming languages and development kits — like Python-based Qiskit, Google’s Cirq, Microsoft’s Q#, and D-Wave’s Ocean SDK — lowers the entry barrier for developers and organizations. These tools provide accessible platforms for experimenting, simulating, and running quantum algorithms both on simulators and real quantum hardware, often through cloud services.

For software developers, mastering quantum software development means gaining a competitive edge in a rapidly advancing field. As quantum hardware matures and becomes more widely available, the demand for skilled quantum programmers will increase, creating new career paths and business opportunities.