# Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis

arXiv: https://arxiv.org/abs/2504.06413 · HTML: https://mlsanity.com/paper/2504.06413/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [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)
- [Exploring the optimality of approximate state preparation quantum circuits with a genetic algorithm](https://mlsanity.com/paper/2210.06411/) (2022)
- [T-Count Optimizing Genetic Algorithm for Quantum State Preparation](https://mlsanity.com/paper/2406.04004/) (2024)

## Built on by (most cited first, 4 of 4)

- [Investigating Quantum Circuit Designs Using Neuro-Evolution](https://mlsanity.com/paper/2602.03840/) (2026)
- [Quantum King-Ring Domination in Chess: A QAOA Approach](https://mlsanity.com/paper/2601.00318/) (2026)
- [Approximate Quantum State Preparation Through Proximal Policy Optimization](https://mlsanity.com/paper/2607.21121/) (2026)
- [Evolving Hybrid Quantum-Classical Architectures for Image Classification](https://mlsanity.com/paper/2610.03220/) (2026)

## Abstract (arXiv)

Quantum computing leverages the unique properties of qubits and quantum parallelism to solve problems intractable for classical systems, offering unparalleled computational potential. However, the optimization of quantum circuits remains critical, especially for noisy intermediate-scale quantum (NISQ) devices with limited qubits and high error rates. Genetic algorithms (GAs) provide a promising approach for efficient quantum circuit synthesis by automating optimization tasks. This work examines the impact of various mutation strategies within a GA framework for quantum circuit synthesis. By analyzing how different mutations transform circuits, it identifies strategies that enhance efficiency and performance. Experiments utilized a fitness function emphasizing fidelity, while accounting for circuit depth and T operations, to optimize circuits with four to six qubits. Comprehensive hyperparameter testing revealed that combining delete and swap strategies outperformed other approaches, demonstrating their effectiveness in developing robust GA-based quantum circuit optimizers.