# AutoQC: Automated Synthesis of Quantum Circuits Using Neural Network

arXiv: https://arxiv.org/abs/2210.02766 · HTML: https://mlsanity.com/paper/2210.02766/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems](https://mlsanity.com/paper/1911.11071/) (2019)
- [Optimizing Quantum Error Correction Codes with Reinforcement Learning](https://mlsanity.com/paper/1812.08451/) (2018)
- [Q#: Enabling scalable quantum computing and development with a high-level domain-specific language](https://mlsanity.com/paper/1803.00652/) (2018)
- [Relaxed Peephole Optimization: A Novel Compiler Optimization for Quantum Circuits](https://mlsanity.com/paper/2012.07711/) (2020)

## Built on by (most cited first, 2 of 2)

- [Optimizing the non-Clifford-count in unitary synthesis using Reinforcement Learning](https://mlsanity.com/paper/2509.21709/) (2025)
- [Optimal Quantum Circuit Design via Unitary Neural Networks](https://mlsanity.com/paper/2408.13211/) (2024)

## Abstract (arXiv)

While the ability to build quantum computers is improving dramatically, developing quantum algorithms is limited and relies on human insight and ingenuity. Although a number of quantum programming languages have been developed, it is challenging for software developers who are not familiar with quantum computing to learn and use these languages. It is, therefore, necessary to develop tools to support developing new quantum algorithms and programs automatically. This paper proposes AutoQC, an approach to automatically synthesizing quantum circuits using the neural network from input and output pairs. We consider a quantum circuit a sequence of quantum gates and synthesize a quantum circuit probabilistically by prioritizing with a neural network at each step. The experimental results highlight the ability of AutoQC to synthesize some essential quantum circuits at a lower cost.