# Classi|Qrangle Towards a Translation Framework To Bridge The Classical-Quantum Programming Gap

arXiv: https://arxiv.org/abs/2406.06764 · HTML: https://mlsanity.com/paper/2406.06764/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Quantum machine learning: a classical perspective](https://mlsanity.com/paper/1707.08561/) (2017)
- [Quantum computing for energy systems optimization: Challenges and opportunities](https://mlsanity.com/paper/2003.00254/) (2020)
- [Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications](https://mlsanity.com/paper/2202.11200/) (2022)
- [Bayesian Deep Learning on a Quantum Computer](https://mlsanity.com/paper/1806.11463/) (2018)
- [Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems](https://mlsanity.com/paper/1910.13045/) (2019)
- [An Extensive Comparison of Static Application Security Testing Tools](https://mlsanity.com/paper/2403.09219/) (2024)
- [Software Engineering for Quantum Programming: How Far Are We?](https://mlsanity.com/paper/2203.16969/) (2022)
- [QDB: From Quantum Algorithms Towards Correct Quantum Programs](https://mlsanity.com/paper/1811.05447/) (2018)
- [QCSHQD: Quantum computing as a service for Hybrid classical-quantum software development: A Vision](https://mlsanity.com/paper/2403.08663/) (2024)
- [Software Pipelining for Quantum Loop Programs](https://mlsanity.com/paper/2012.12700/) (2020)
- [Dynamic Optimization on Quantum Hardware: Feasibility for a Process Industry Use Case](https://mlsanity.com/paper/2311.07310/) (2023)

## Abstract (arXiv)

Quantum computing, albeit readily available as hardware or emulated on the cloud, is still far from being available in general regarding complex programming paradigms and learning curves. This vision paper introduces Classi|Qrangle, a translation framework idea to bridge Classical and Quantum Computing by translating high-level programming languages, e.g., Python or C++, into a low-level language, e.g., Quantum Assembly. Our idea paper serves as a blueprint for ongoing efforts in quantum software engineering, offering a roadmap for further Classi|Qrangle development to meet the diverse needs of researchers and practitioners. Classi|Qrangle is designed to empower researchers and practitioners with no prior quantum experience to harness the potential of hybrid quantum computation. We also discuss future enhancements to Classi|Qrangle, including support for additional quantum languages, improved optimization strategies, and integration with emerging quantum computing platforms.