# Quantum Circuit Design Search

arXiv: https://arxiv.org/abs/2012.04046 · HTML: https://mlsanity.com/paper/2012.04046/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 13 of 13)

- [Reinforcement learning for optimization of variational quantum circuit architectures](https://mlsanity.com/paper/2103.16089/) (2021)
- [Quantum Architecture Search via Deep Reinforcement Learning](https://mlsanity.com/paper/2104.07715/) (2021)
- [Recent advances for quantum classifiers](https://mlsanity.com/paper/2108.13421/) (2021)
- [Quantum Architecture Search: A Survey](https://mlsanity.com/paper/2406.06210/) (2024)
- [Quantum Architecture Search via Continual Reinforcement Learning](https://mlsanity.com/paper/2112.05779/) (2021)
- [A semi-agnostic ansatz with variable structure for quantum machine learning](https://mlsanity.com/paper/2103.06712/) (2021)
- [A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis](https://mlsanity.com/paper/2401.07054/) (2024)
- [Challenges for Reinforcement Learning in Quantum Circuit Design](https://mlsanity.com/paper/2312.11337/) (2023)
- [Reinforcement Learning for Variational Quantum Circuits Design](https://mlsanity.com/paper/2409.05475/) (2024)
- [Quantum Architecture Search with Unsupervised Representation Learning](https://mlsanity.com/paper/2401.11576/) (2024)
- [Q-Fusion: Diffusing Quantum Circuits](https://mlsanity.com/paper/2504.20794/) (2025)
- [Qubit-Wise Architecture Search Method for Variational Quantum Circuits](https://mlsanity.com/paper/2403.04268/) (2024)
- [Dynamic Inhomogeneous Quantum Resource Scheduling with Reinforcement Learning](https://mlsanity.com/paper/2405.16380/) (2024)

## Abstract (arXiv)

This article explores search strategies for the design of parameterized quantum circuits. We propose several optimization approaches including random search plus survival of the fittest, reinforcement learning both with classical and hybrid quantum classical controllers and Bayesian optimization as decision makers to design a quantum circuit in an automated way for a specific task such as multi-labeled classification over a dataset. We introduce nontrivial circuit architectures that are arduous to be hand-designed and efficient in terms of trainability. In addition, we introduce reuploading of initial data into quantum circuits as an option to find more general designs. We numerically show that some of the suggested architectures for the Iris dataset accomplish better results compared to the established parameterized quantum circuit designs in the literature. In addition, we investigate the trainability of these structures on the unseen dataset Glass. We report meaningful advantages over the benchmarks for the classification of the Glass dataset which supports the fact that the suggested designs are inherently more trainable.