Q#: Enabling scalable quantum computing and development with a high-level domain-specific language
Not on the map. Why?
Abstract (arXiv)
Quantum computing exploits quantum phenomena such as superposition and entanglement to realize a form of parallelism that is not available to traditional computing. It offers the potential of significant computational speed-ups in quantum chemistry, materials science, cryptography, and machine learning. The dominant approach to programming quantum computers is to provide an existing high-level language with libraries that allow for the expression of quantum programs. This approach can permit computations that are meaningless in a quantum context; prohibits succinct expression of interaction between classical and quantum logic; and does not provide important constructs that are required for quantum programming. We present Q#, a quantum-focused domain-specific language explicitly designed to correctly, clearly and completely express quantum algorithms. Q# provides a type system, a tightly constrained environment to safely interleave classical and quantum computations; specialized syntax, symbolic code manipulation to automatically generate correct transformations of quantum operations, and powerful functional constructs which aid composition.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Mar 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 0; 2025: 2; 2026: 1.
7 papers.
- 2021Recent advances for quantum classifierscited by 35
- 2022Quantum Neural Network Classifiers: A Tutorialcited by 13
- 2022AutoQC: Automated Synthesis of Quantum Circuits Using Neural Networkcited by 2
- 2025Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Studycited by 1
- 2022Differentiable Quantum Programming with Unbounded Loopscited by 0
- 2025TorchQuantumDistributedcited by 0
- 2026Transpiler Autotuning with Predictive Models for Quantum Circuit Optimizationcited by 0
- 2026Transpiler Autotuning with Predictive Models for Quantum Circuit Optimization31 Jul 2026
- 2025TorchQuantumDistributed24 Nov 2025
- 2025Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study8 Jan 2025
- 2022Differentiable Quantum Programming with Unbounded Loops8 Nov 2022
- 2022AutoQC: Automated Synthesis of Quantum Circuits Using Neural Network6 Oct 2022
- 2022Quantum Neural Network Classifiers: A Tutorial6 Jun 2022
- 2021Recent advances for quantum classifiers30 Aug 2021