# ProjectQ: An Open Source Software Framework for Quantum Computing

arXiv: https://arxiv.org/abs/1612.08091 · HTML: https://mlsanity.com/paper/1612.08091/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2016.

## Built on by (most cited first, 6 of 6)

- [Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation](https://mlsanity.com/paper/2211.08737/) (2022)
- [Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning](https://mlsanity.com/paper/2206.15284/) (2022)
- [Challenges for Reinforcement Learning in Quantum Circuit Design](https://mlsanity.com/paper/2312.11337/) (2023)
- [Demystifying Higher-Order Graph Neural Networks](https://mlsanity.com/paper/2406.12841/) (2024)
- [Supervised learning of random quantum circuits via scalable neural networks](https://mlsanity.com/paper/2206.10348/) (2022)
- [TorchQuantumDistributed](https://mlsanity.com/paper/2511.19291/) (2025)

## Abstract (arXiv)

We introduce ProjectQ, an open source software effort for quantum computing. The first release features a compiler framework capable of targeting various types of hardware, a high-performance simulator with emulation capabilities, and compiler plug-ins for circuit drawing and resource estimation. We introduce our Python-embedded domain-specific language, present the features, and provide example implementations for quantum algorithms. The framework allows testing of quantum algorithms through simulation and enables running them on actual quantum hardware using a back-end connecting to the IBM Quantum Experience cloud service. Through extension mechanisms, users can provide back-ends to further quantum hardware, and scientists working on quantum compilation can provide plug-ins for additional compilation, optimization, gate synthesis, and layout strategies.