Top 10 Quantum Programming Languages to Learn in 2023

Top 10 Quantum Programming Languages to Learn in 2023


Quantum computing promises to revolutionize computation by harnessing the strange properties of quantum physics. Programming these quantum computers requires fundamentally different languages tailored for the quantum paradigm of qubits, superposition, entanglement and probabilistic results.

In this guide, we will explore the top 10 quantum programming languages that developers should get familiar with as quantum computers become more mainstream in the coming years.

We will cover established languages like Q# by Microsoft as well as emerging entrants like Amazon Braket and Google’s Cirq. By learning these languages now, you’ll be at the forefront of the quantum revolution! Let’s dive in.

Top 10 Quantum Programming Languages:

#1 Q#

Q# (Q sharp) is a flagship quantum programming language developed by Microsoft. Some key features:

  • Integrates with .NET and C# for hybrid quantum-classical programming
  • Fully featured with typing, namespaces, operators and control flow
  • Compiles to optimized quantum gates on various quantum hardware
  • Simulator allows executing locally
  • Visual Studio and Python integration
  • Focus on usability by classical developers

If you’re already familiar with languages like C#, Q# provides a very accessible starting point for quantum programming with a similar syntax.

#2 PyQuil

PyQuil is a quantum programming library by Rigetti Computing for Python. Features:

  • Near-term quantum computing focus with shortcuts like amplitude estimation
  • Hybrid algorithms combine quantum and classical executors
  • Quantum VM engine and simulator
  • Forest API integrates with cloud platform
  • Extensible and embeddable from Python
  • Active ecosystem of libraries like Grove on top of PyQuil

For Python developers, PyQuil offers the most natural entrypoint to leverage existing classical code.

#3 Cirq

Cirq is an open source framework by Google for writing, optimizing and testing quantum algorithms using Python:

  • Built on restriction that algorithms map to realistic near-term hardware
  • Synthesizes quantum gates, schedules them optimally
  • Simulates with error models of real hardware
  • Integrates with TensorFlow Quantum for hybrid coding
  • Interoperable with OpenFermion for quantum chemistry
  • Applicable for NISQ algorithms like VQE and QAOA

If you want to focus on practical near-term algorithms, Cirq provides powerful modeling and simulation tools.

#4 Amazon Braket

Amazon Braket provides a fully managed quantum development experience:

  • Managed Jupyter notebooks to build algorithms in Python
  • Integration with AWS for hybrid algorithms
  • Simulate quantum performance on simulated hardware
  • Test programs on actual quantum hardware like Rigetti, D-Wave, IonQ
  • Visual interface to model algorithms before coding
  • S3 storage for saving programs
  • Automated metric tracking

For quick prototyping of quantum programs without setup overhead, Braket offers an easy cloud-based workflow.

#5 Qiskit

Qiskit by IBM is an open-source SDK for working with quantum computers and simulators at level of circuits, pulses, and algorithms. Features:

  • Write quantum code in Python using readable APIs
  • Extensible with hooks to write custom logic
  • Pulse-level control for advanced users
  • High performance simulators for prototyping algorithms
  • Integrated Jupyter notebooks for education
  • Developer libraries for algebra, optimization etc.
  • IBM Quantum Experience integration

Qiskit balances usability with advanced controls making it versatile for research and education.

#6 Silq

Silq is a high-level quantum language by ETH Zurich for algorithm programmers:

  • Python-like syntax focused on readability
  • Type system ensures correct manipulation of qubits
  • Compiles to low-level quantum circuits
  • Annotations transform classical functions to reversible quantum versions
  • Optimization and uncomputation of ancilla qubits
  • Can interoperate with Python libraries
  • Executes on simulators and hardware backends

Silq makes expressing quantum algorithms at a high conceptual level easy for those with less hardware expertise.

#7 LIQUi|>

LIQUi|> (LIQUID) is an open source functional quantum programming language from Microsoft Research. Features:

  • Strongly statically typed with type inference
  • Pure functional for side-effect free programming
  • Higher order functions, pattern matching, polymorphism
  • Compiles to F# to run on simulators and Azure Quantum
  • Focus on enabling verification of programs
  • Integrates with F#/.NET/C# ecosystem
  • Still research-stage but rapidly evolving

LIQUi|> brings functional programming principles like immutability to aid correctness of quantum code.

#8 Quipper

Quipper is a functional programming language for quantum computing created by a team of researchers from Canada, France, and the UK. Features:

  • Based on Haskell with influences from OCaml and Scheme
  • Statically typed with type inference
  • Lambda calculus foundation enables formal reasoning
  • Compiles high-level algorithms to low-level quantum assembly
  • Embedded quantum pseudo-code simplifies complex circuit constructions
  • Integrates with classical Haskell libraries
  • Available on simulators and physical hardware

Quipper leverages lessons from functional languages to create robust quantum routines.

#9 Qmasm

Qmasm provides a lightweight assembly-style language for quantum programming. Features:

  • Textual assembly language for quantum instruction sequences
  • Maps clearly to quantum circuits
  • Integrates with Python tooling for pre/post-processing
  • Executes on various simulators as well as actual hardware
  • Academic open source project with growing community

Qmasm offers a low-level quantum programming model while integrating smoothly with Python toolchains.

#10 λ (Landa)

λ (Landa) explores quantum functional programming:

  • Based on Typed Lambda Calculus for modeling quantum computations
  • Modules, algebraic data types, pattern matching
  • Monads model quantum control flow
  • Equational reasoning principles aid formal verification
  • Compiler to low-level quantum IR
  • Focus on productivity and safety
  • Developed at Carnegie Mellon University

Landa is on the cutting edge of research into making quantum programming easier and less error-prone for developers.

Quantum Programming Learning Paths

The ideal path to get started with quantum programming depends on your current experience:

  • Beginners – Start with high-level languages like Python libraries (PyQuil, Qiskit) or managed services (Braket)
  • Developers – Pick a language aligned with current skills – C# developers may prefer Q#, Python developers can use Cirq
  • Physicists – Lower-level languages like Qiskit allow finer hardware control
  • Theorists – Leverage languages with formal foundations like Quipper or LIQUi|>
  • Open Source – Contribute to projects like Q# or Qiskit on GitHub

Don’t get overwhelmed – just start exploring libraries for the language you know best. The field is rapidly evolving so focus on fundamentals rather than any specific syntax.

The Future of Quantum Programming

We are still in the very initial days of the quantum computing revolution. Near-term roadmaps envision:

  • Hybrid algorithms integrating with cloud services
  • Development tools focused on usability and simulation
  • Training quantum programmers from physics and CS backgrounds
  • New languages tailored for different domains like finance or chemistry
  • Frameworks for verifying and debugging quantum programs
  • Ubiquitous access via cloud services like Braket and Azure Quantum
  • Massproduced modular quantum hardware

It’s an exciting time to get involved and move up the learning curve as the industry matures.


Quantum computing opens up an enormous new computing paradigm for developers. Programming these machines requires learning to think quantum – forgetting classical limitations.

This guide provided an overview of pioneering quantum programming languages that are making QC more accessible to classical developers. The available options range from extensions of existing languages like Python to entirely new languages like Q# and LIQUi|>.

I encourage you to pick an approach aligned with your current skills and start experimenting. Run algorithms locally using simulators before trying time on real hardware. Learn by contributing to quantum software projects. Attend conferences and get involved in the growing quantum programming community.

By starting to learn quantum-centric languages now, you’ll be among the vanguard ready to code the next generation of transformative applications across chemical simulation, optimization, finance, AI and more as quantum matures from promise to practicality.

Frequently Asked Questions

How is quantum programming different from classical programming?

Some key differences between quantum and classical programming:

  • Deals with qubits instead of bits – qubits can be in superposition of 0 and 1
  • Probabilistic results instead of deterministic
  • Heisenberg uncertainty principles limit simultaneous measurement
  • Reversible actions required with no data loss
  • Entangled states with interdependent qubits
  • Non-trivial transforming classical programs into quantum equivalents

New languages provide constructs tailored to these quantum principles without classical assumptions.

What are some examples of practical quantum algorithms?

Somequantum algorithms with practical applications:

  • Shor’s algorithm – efficient integer factorization for cryptography
  • Grover’s algorithm – speed up unsorted database search
  • Quantum annealing – optimize planning/scheduling problems
  • Quantum simulation – model quantum systems like chemistry
  • QML – quantum machine learning models
  • Linear systems solver – solve linear equations efficiently

As hardware improves, more quantum advantage will become practically achievable.

What skills are required to get started with quantum programming?

Some skills useful to learn quantum computing:

  • Solid grasp of linear algebra – vectors, matrices, tensors
  • Statistics and probabilities – expectation, distributions
  • Some physics basics – quantum states, measurement
  • Algorithms involving search, sorting, simulations
  • Optimizations like dynamic programming
  • Existing programming experience in any language

A mix of math/physics/CS helps, but most concepts can be learned on the fly. Programming skill is foundational.

Are there publicly accessible quantum computers to try out code?

Some ways to run quantum code today without deep expertise:

  • IBM Quantum Experience – Gate model quantum computers
  • Amazon Braket – Various hardware backends
  • Azure Quantum – IonQ, Honeywell, Quantum Circuits, Inc
  • Google Cloud – Quantum annealing machines
  • D-Wave Leap – Managed realtime access

These provide cloud access to try algorithms on real quantum processors via services.

What are some good online resources to learn quantum computing?

Some useful quantum computing learning resources:

  • Qiskit Textbook – Broad overview with Python demos
  • Microsoft Learn Quantum – Interactive modules using Q#
  • Coursera Specializations – Foundations by Maryland University
  • Udemy Courses – Programming courses in Q#, Qiskit, Cirq
  • edX Quantum Courses – Berkeley, MIT xPro quantum courses
  • YouTube Channels – Great free video content
  • Qiskit Blog – Tutorials and research from IBM
  • Papers – Arxiv, Nature for latest research

There is an abundance of structured learning content on quantum computing topics today.

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