Quantum programming languages are rapidly emerging as crucial tools in the unfolding era of quantum computing. This new breed of programming language is enabling developers and researchers to interact with qubits and design algorithms that may soon outperform their classical counterparts by orders of magnitude.
As of 2025, more than 37 distinct quantum programming languages have been introduced, each tailored to specific models, hardware, and use-cases of quantum computing. Collectively, these languages reflect the diverse approaches in quantum computation, ranging from gate-based systems and variational circuits to adiabatic models and hybrid classical-quantum algorithms. [1]
Below, I’ve highlighted the leading quantum programming languages that enable developers to effectively harness the peculiarities of quantum mechanics for solving complex problems.
Did you know?GitHub hosts over 21,000 quantum software repositories, reflecting a 200% growth since 2017. These repositories have attracted contributions from over 10,000 unique developers, with code commits surpassing 1.2 million lines collectively. [2]
Table of Contents
13. Qunity
Initial Release: 2022Created by: Independent researchers and open-source contributors
Unique Strength: Quantum-first approach and clean abstraction model
Best for: Researchers looking for a lightweight toolset without vendor lock-in
Qunity is a lesser-known but forward-thinking quantum programming language and framework that blends the clarity of classical programming models with the unique paradigms of quantum mechanics.
It is built from the ground up to be a quantum-native language, enforcing quantum mechanical constraints (such as no-cloning and unitary evolution) at both the type system and logic level.
While it draws inspiration from classical programming languages like C and Python, it adapts the syntax and semantics to be naturally compatible with quantum computing principles, such as linearity, reversibility, superposition, and entanglement. [3]
Qunity’s design is modular and intended to be backend-agnostic, with support for simulators and compatibility layers being developed to target real quantum processors in the future.
12. TKET
Initial Release: 2019Created by: Quantinuum
Unique Strength: Intelligently handles hardware constraints
Best for: Hardware-agnostic circuit optimization; production-level tools
TKET acts primarily as an intermediate-level optimizing compiler and execution framework that interfaces between high-level quantum programs and various quantum hardware targets.
TKET focuses on quantum circuit optimization, gate synthesis, and qubit routing. It transforms abstract quantum algorithms into hardware-compatible instructions with minimal depth and error rates.
Its ability to intelligently handle hardware constraints (such as qubit connectivity limitations, gate fidelities, and native instruction sets) enables it to generate more efficient circuits with lower gate counts and shorter execution times.
In independent benchmarks, TKET has shown up to 40% lower gate count and 25–30% faster execution times compared to native compilers in some use cases.
11. ProjectQ
Created by: ETH Zurich
Unique Strength: Compiler-oriented, modular design
Best for: Quantum compiler research and educational purposes
ProjectQ provides a modular and extensible quantum computing platform for researchers, developers, and educators.
Written in Python, it was one of the early quantum SDKs that emphasized a compiler-based architecture, where quantum programs are translated into intermediate representations before execution on simulators or real quantum hardware.
ProjectQ emphasizes modularity, allowing developers to define quantum programs in a Python-like style, compile them using customizable pipelines, and execute them on either built-in simulators or backends, such as IBM Quantum Experience. Plus, the compiler-centric design allows fine-grained control over optimization, gate decomposition, and target-specific translation. [4]
It also supports quantum circuit emulation, wavefunction simulation, unitary matrix emulation, and can serve as an experimental playground for compiler research, quantum error correction, and optimization studies.
10. Quantum Computation Language (QCL)
Initial Release: 2000Created by: Bernhard Ömer
Unique Strength: Clear, low-level access to the quantum state
Best for: Learning classical-quantum hybrid structure
QCL is one of the earliest high-level programming languages built specifically for quantum computing.
At a time when quantum programming was largely done at the mathematical or circuit level, QCL introduced structured syntax, variables, conditionals, loops, and modular functions — essentially laying a foundation for later high-level quantum languages.
It separates classical and quantum computation, where classical registers manage control flow, while quantum registers (quregs) store and manipulate quantum states. The language introduced many of the now-common programming constructs for quantum computing, such as unitary operators, measurement instructions, reversible computing, and ancilla qubits for temporary use.
Although QCL is not tied to any commercial quantum hardware, it remains a valuable tool in academic and research settings, particularly for teaching and simulating quantum algorithms.
9. Strawberry Fields
Created by: Xanadu
Unique Strength: Exclusive focus on CV quantum computing
Best for: Simulating and programming photonic quantum systems
Strawberry Fields is a full-stack, open-source quantum programming library tailored for continuous-variable (CV) quantum computing. It is specifically engineered for quantum photonic circuits that use quantum states of light (like squeezed states, coherent states, and Gaussian states) to perform computations.
At its core, Strawberry Fields leverages the Blackbird quantum assembly language. This enables developers to design, simulate, and optimize photonic quantum circuits using both gate-based models and machine learning workflows.
It can be used in conjunction with PennyLane (another Xanadu project) to facilitate hybrid quantum-classical computations and machine learning applications. The platform is well-suited for use cases in quantum machine learning, quantum chemistry, CV quantum cryptography, and quantum optics research. [5]
8. Ocean
Initial Release: 2018Created by: D-Wave Systems
Unique Strength: Designed for quantum annealing; supports hybrid solvers
Best for: Tackling combinatorial optimization problems
Ocean is an open-source quantum programming framework specifically designed to help users develop and optimize problems for quantum annealers — a very different paradigm from gate-based quantum computers. [6]
It’s not a programming language in the traditional sense, but a toolchain built in Python that translates user-defined problems (mainly optimization problems) into a form solvable by D-Wave’s quantum annealing hardware.
Ocean’s modular architecture makes it highly extensible for developers and researchers working on custom solvers or optimization heuristics. It is best suited for tackling combinatorial optimization problems such as vehicle routing, portfolio optimization, scheduling, and machine learning tasks.
Plus, Ocean seamlessly integrates with D-Wave’s Leap cloud service, providing developers with immediate access to quantum hardware and resources. In 2025, D-Wave announced the general availability of its Advantage2 quantum computing system, featuring over 4,400 qubits. Ocean has been updated to fully support this next-generation system.
7. QuTiP
Initial Release: 2012Created by: Johannes Johansson, Paul Nation, & and collaborators
Unique Strength: Physics-focused simulation power
Best for: Theoretical research and modeling
QuTiP is a comprehensive open-source framework that focuses on simulating the physics of quantum systems, particularly in continuous-time evolution, master equations, and non-unitary dynamics.
Written in Python and optimized using NumPy, SciPy, and Cython, QuTiP is efficient for numerical computations. It allows researchers to model and solve time-dependent Schrödinger equations, Lindblad master equations, and Heisenberg equations of motion, which are central to quantum mechanics. [7]
QuTiP also provides a suite of solvers, including mesolve for Lindblad master equations, sesolve for Schrödinger equations, and mcsolve for Monte Carlo simulations. These solvers enable the study of both closed and open quantum systems under various conditions.
These capabilities make it widely used in fields such as quantum optics, quantum thermodynamics, cavity QED, and superconducting qubit research.
6. PennyLane
Created by: Xanadu
Unique Strength: Integrates with classical machine learning ecosystems
Best for: Quantum machine learning; hybrid classical-quantum models
PennyLane is an open-source Python-based quantum computing library that integrates quantum computing with machine learning workflows, offering tools for automatic differentiation of quantum circuits.
It bridges quantum computation and classical optimization by leveraging frameworks such as PyTorch, TensorFlow, and JAX. This allows developers to embed quantum nodes into classical machine learning models.
PennyLane supports various quantum hardware and simulators through its plugin system, enabling developers to run quantum circuits on different backends without modifying their code. This flexibility makes it a valuable tool for both research and practical applications in quantum computing. [8]
The library also offers specialized functionalities for quantum chemistry, such as differentiable Hartree-Fock solvers and tools for constructing molecular Hamiltonians. These features allow researchers to perform quantum chemistry simulations with gradient-based optimization techniques.
5. OpenQASM
Created by: IBM Research
Unique Strength: Supports hardware neutrality, facilitates interoperability
Best for: Defining low-level circuits and gate sequences
OpenQASM (Open Quantum Assembly Language) is a hardware-agnostic, intermediate representation for quantum circuits that provides a low-level, assembly-style syntax for quantum gate operations.
The language is particularly focused on expressing quantum circuits in a machine-readable and human-readable format. It allows for the precise description of gate-level instructions, including qubit allocation, quantum gate application, measurements, classical register interactions, and conditional logic.
OpenQASM has evolved into a widely accepted standard for expressing quantum programs in a structured, declarative form. Recent research has focused on enabling the verification and formalization of hybrid quantum-classical computing with OpenQASM 3, highlighting the language’s potential for rigorous program analysis and validation.
4. PyQuil
Initial Release: 2017Created by: Rigetti Computing
Unique Strength: Parametric compilation, realistic simulation layer
Best for: Executing programs on Rigetti hardware and simulators
PyQuil is an open-source quantum programming library built for creating and running quantum programs using the Quil (Quantum Instruction Language) protocol.
Written in Python, PyQuil serves as the primary tool for interacting with Rigetti’s Forest platform and Aspen quantum processors, which are based on superconducting qubit architectures. Unlike Python-based abstractions in other frameworks, Quil gives developers more transparency into what actually runs on the quantum hardware, which is invaluable for debugging and performance tuning. [9]
It is especially geared toward NISQ (Noisy Intermediate-Scale Quantum) computing, providing developers an expressive yet low-level interface to construct and simulate quantum circuits.
PyQuil offers a strong blend of abstraction and optimization, along with a realistic simulation layer (QVM) that closely mimics hardware behavior. Developers can compile circuits to match the real hardware constraints of Rigetti’s devices, such as qubit connectivity, native gate sets, and noise profiles.
Combined with access to Rigetti’s Aspen QPUs, this makes PyQuil a practical and high-fidelity tool for research and testing.
3. Q#
Created by: Microsoft
Unique Strength: Built-in simulation tools, modular quantum libraries
Best for: Enterprise-grade algorithm development
Q# (Q-sharp) is a domain-specific quantum programming language that uses a strong type system and enforces strict separation between quantum data and classical data to minimize programming errors.
Unlike frameworks like Qiskit and Cirq that are built on general-purpose programming languages like Python, Q# is designed from the ground up specifically for quantum computing.
Q# stands out for its formal structure, strong type safety, and long-term scalability. It features strong static typing and compile-time error checking, which significantly reduces the risk of bugs. One of its key strengths is built-in resource estimation tools, which enable developers to not only simulate quantum circuits but also evaluate their cost in terms of qubits, gate counts, and circuit depth.
It comes with a rich library of pre-built quantum operations, such as quantum Fourier transform, amplitude amplification, and Grover’s search. Plus, it is deeply integrated into Microsoft’s broader Azure Quantum platform as well as Visual Studio, Visual Studio Code, and Jupyter Notebooks. [10]
2. Cirq
Created by: Google AI Quantum Team
Unique Strength: Hardware-oriented, precision-level control
Best for: Custom circuits for NISQ-era devices, quantum simulations
Cirq is an open-source quantum programming framework designed primarily to support NISQ (Noisy Intermediate-Scale Quantum) devices. It offers developers fine-grained control over quantum circuits, especially where hardware topology and noise properties matter.
Cirq utilizes a moment-based architecture. It organizes operations into time-based “Moments,” allowing for better simulation of execution timing and making it easier to incorporate realistic noise and decoherence models. [11]
Unlike higher-level frameworks that focus on abstract quantum algorithms, Cirq emphasizes control at the gate and circuit scheduling level. This enables researchers to experiment with hardware-aware optimizations and benchmark quantum operations.
Cirq is often used as the base programming layer for TensorFlow Quantum. It is also the native language for interacting with Google’s superconducting quantum processors, such as Sycamore and Bristlecone.
1. Qiskit
Created by: IBM Research
Unique Strength: Deep vertical integration with IBM’s quantum hardware
Best for: Circuit design, quantum algorithm prototyping, hybrid simulations
Qiskit is an open-source quantum programming language that empowers developers and researchers to engage with quantum computing from both theoretical and practical perspectives, ranging from simulating quantum circuits to deploying them on IBM’s superconducting quantum processors.
Built in Python, Qiskit supports the gate-based model of quantum computation and has been tightly integrated with IBM’s quantum cloud hardware.
The platform has evolved beyond a basic SDK into a comprehensive stack that includes high-level algorithm libraries (such as Qiskit Machine Learning, Nature, Optimization), mid-level circuit builders, and even pulse-level control through Qiskit Pulse. This layered architecture enables developers to create, experiment, and optimize quantum programs at every abstraction level.
One of Qiskit’s most groundbreaking features is its real-time access to actual quantum hardware via IBM Quantum Experience, which hosts quantum processors with 5, 16, 27, and 127+ qubits. This has given thousands of developers hands-on experience with real quantum noise and device limitations.
Over the past few years, Qiskit has emerged as one of the most accessible and widely adopted platforms in quantum computing. It has been installed over 6 million times, with around 300,000 new installations every month. With more than 2,000 forks and over 8,000 contributions on GitHub, Qiskit has also enabled the execution of over 3 trillion quantum circuits to date. [12]
Read More
- 21 Most Interesting Facts About Quantum Computers
- 15 Quantum Processors That Feature New Computing Paradigm
- Felipe Ferreira, An exploratory study on the usage of quantum programming languages, ScienceDirect
- Krishna Upadhyay, Analyzing the evolution and maintenance of quantum computing repositories, arXiv
- Finn Voichick, A unified language for quantum and classical computing, arXiv
- ProjectQ, An open-source software effort for quantum computing, arXiv
- Documentation, PennyLane-Strawberry fields plugin, PennyLane
- Documentation, How to optimize business problems using quantum-classical hybrid solvers, D-Wave
- Neill Lambert, The quantum toolbox in Python, arXiv
- Quantum Devices, PennyLane’s quantum device ecosystem with 40+ integrated options, PennyLane
- Documentation, pyQuil allows you to build and execute Quil programs using Python, pyQuil
- Qsharp Overview, Introduction to the quantum programming language Q#, Microsoft
- Reference, A time-slice of operations within a circuit, Google
- Muhammad AbuGhanem, IBM quantum computers: evolution, performance, and future directions, Springer
