# Investigating Quantum Circuit Designs Using Neuro-Evolution

arXiv: https://arxiv.org/abs/2602.03840 · HTML: https://mlsanity.com/paper/2602.03840/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 7 of 7)

- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [Absence of Barren Plateaus in Quantum Convolutional Neural Networks](https://mlsanity.com/paper/2011.02966/) (2020)
- [Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems](https://mlsanity.com/paper/1911.11071/) (2019)
- [Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution](https://mlsanity.com/paper/1902.02390/) (2019)
- [Evolutionary Optimization for Designing Variational Quantum Circuits with High Model Capacity](https://mlsanity.com/paper/2412.12484/) (2024)
- [Evolutionary Quantum Architecture Search for Parametrized Quantum Circuits](https://mlsanity.com/paper/2208.11167/) (2022)
- [Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis](https://mlsanity.com/paper/2504.06413/) (2025)

## Built on by (most cited first, 1 of 1)

- [Evolving Hybrid Quantum-Classical Architectures for Image Classification](https://mlsanity.com/paper/2610.03220/) (2026)

## Abstract (arXiv)

Designing effective quantum circuits remains a central challenge in quantum computing, as circuit structure strongly influences expressivity, trainability, and hardware feasibility. Current approaches, whether using manually designed circuit templates, fixed heuristics, or automated rules, face limitations in scalability, flexibility, and adaptability, often producing circuits that are poorly matched to the specific problem or quantum hardware. In this work, we propose the Evolutionary eXploration of Augmenting Quantum Circuits (EXAQC), an evolutionary approach to the automated design and training of parameterized quantum circuits (PQCs) which leverages and extends on strategies from neuroevolution and genetic programming. The proposed method jointly searches over gate types, qubit connectivity, parameterization, and circuit depth while respecting hardware and noise constraints. The method supports both Qiskit and Pennylane libraries, allowing the user to configure every aspect. This work highlights evolutionary search as a critical tool for advancing quantum machine learning and variational quantum algorithms, providing a principled pathway toward scalable, problem-aware, and hardware-efficient quantum circuit design. Preliminary results demonstrate that circuits evolved on classification tasks are able to achieve over 90% accuracy on most of the benchmark datasets with a limited computational budget, and are able to emulate target circuit quantum states with high fidelity scores.