# A Software Methodology for Compiling Quantum Programs

arXiv: https://arxiv.org/abs/1604.01401 · HTML: https://mlsanity.com/paper/1604.01401/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2016.

## Built on by (most cited first, 5 of 5)

- [Theory of overparametrization in quantum neural networks](https://mlsanity.com/paper/2109.11676/) (2021)
- [Theory for Equivariant Quantum Neural Networks](https://mlsanity.com/paper/2210.08566/) (2022)
- [Variational quantum compiling with double Q-learning](https://mlsanity.com/paper/2103.11611/) (2021)
- [Quantum compiling with a variational instruction set for accurate and fast quantum computing](https://mlsanity.com/paper/2203.15574/) (2022)
- [Q-fid: Quantum Circuit Fidelity Improvement with LSTM Networks](https://mlsanity.com/paper/2303.17523/) (2023)

## Abstract (arXiv)

Quantum computers promise to transform our notions of computation by offering a completely new paradigm. To achieve scalable quantum computation, optimizing compilers and a corresponding software design flow will be essential. We present a software architecture for compiling quantum programs from a high-level language program to hardware-specific instructions. We describe the necessary layers of abstraction and their differences and similarities to classical layers of a computer-aided design flow. For each layer of the stack, we discuss the underlying methods for compilation and optimization. Our software methodology facilitates more rapid innovation among quantum algorithm designers, quantum hardware engineers, and experimentalists. It enables scalable compilation of complex quantum algorithms and can be targeted to any specific quantum hardware implementation.