Genetic Transformer-Assisted Quantum Neural Networks for Optimal Circuit Design

TL;DR

This paper presents GTQNNs, a hybrid quantum-classical framework that uses a transformer and genetic algorithms to optimize quantum circuits for classification tasks, achieving high accuracy with fewer gates on NISQ hardware.

Contribution

The paper introduces a novel hybrid framework combining transformers and genetic algorithms to optimize quantum neural networks for practical NISQ device implementation.

Findings

GTQNNs match or outperform state-of-the-art quantum models.

GTQNNs require fewer quantum gates, making them suitable for NISQ hardware.

The networks operate far from barren plateaus, indicating effective training.

Abstract

We introduce Genetic Transformer Assisted Quantum Neural Networks (GTQNNs), a hybrid learning framework that combines a transformer encoder with a shallow variational quantum circuit and automatically fine tunes the circuit via the NSGA-II multi objective genetic algorithm. The transformer reduces high-dimensional classical data to a compact, qubit sized representation, while NSGA-II searches for Pareto optimal circuits that (i) maximize classification accuracy and (ii) minimize primitive gate count an essential constraint for noisy intermediate-scale quantum (NISQ) hardware. Experiments on four benchmarks (Iris, Breast Cancer, MNIST, and Heart Disease) show that GTQNNs match or exceed state of the art quantum models while requiring much fewer gates for most cases. A hybrid Fisher information analysis further reveals that the trained networks operate far from barren plateaus; the leading curvature directions increasingly align with the quantum subspace as the qubit budget grows, confirming that the transformer front end has effectively condensed the data. Together, these results demonstrate that GTQNNs deliver competitive performance with a quantum resource budget well suited to present-day NISQ devices.