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International Joint Conference on Neural Networks (IJCNN)

Evolutionary Weight Optimization for Variational Quantum Classifiers

Matheus Costa, Fernando M. de Paula Neto

Published Jun 30, 2025• 0 citations
Publisher

Abstract

Variational Quantum Circuits (VQCs) are viable alternatives for implementing quantum circuits in current quantum processors. VQCs are reduced circuits with parameters that are trained to minimize a loss function for a given problem. In this work, we propose an approach to optimize the parameters of a VQC using Genetic Algorithms (GAs). We explored different GA configurations, on 3 databases - Sonar, Cancer and Banknotes datasets, comparing the proposed approach with 2 classical optimizers based on gradient descent. The results show that genetic algorithms can effectively optimize the weights of variational circuits to achieve high classification accuracy with convergence speed up to 7.3 times (in Sonar Database), 4.8 times (Cancer Database), and 3.7 times (Banknote Database) compared with the traditional optimizers. The experimental results indicate that the genetic algorithm achieves performance metrics that are significantly superior or at least comparable to those obtained by gradient descent-based optimizers, demonstrating the potential and robustness of evolutionary strategies in training variational quantum circuits.

DOI
10.1109/ijcnn64981.2025.11227638

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Variational Quantum Circuits (VQCs) are viable alternatives for implementing quantum circuits in current quantum processors. VQCs are reduced circuits with parameters that are trained to minimize a loss function for a given problem. In this work, we propose an approach to optimize the parameters of a VQC using Genetic Algorithms (GAs). We explored different GA configurations, on 3 databases - Sonar, Cancer and Banknotes datasets, comparing the proposed approach with 2 classical optimizers based on gradient descent. The results show that genetic algorithms can effectively optimize the weights of variational circuits to achieve high classification accuracy with convergence speed up to 7.3 times (in Sonar Database), 4.8 times (Cancer Database), and 3.7 times (Banknote Database) compared with the traditional optimizers. The experimental results indicate that the genetic algorithm achieves performance metrics that are significantly superior or at least comparable to those obtained by gradient descent-based optimizers, demonstrating the potential and robustness of evolutionary strategies in training variational quantum circuits.
Recently, Variational Quantum Circuits (VQCs) are attracting considerable attention among quantum algorithms thanks to their robustness to the noise characterizing the current quantum devices. In detail, VQCs involve parameterized quan-tum circuits to be trained by means of a classical optimizer that makes queries to the quantum device. VQCs play a key role in several applications including quantum classifiers where the Hilbert space is used as feature space. Currently, the most used classical optimizer to learn V QCs is the gradient descent method. However, the so-called barren plateaus issue causes gradients of cost functions to become exceedingly small as the dimension of the classification problem is increased. As consequence, gradient descent method could be not efficient in real-world classification problems. This paper proposes to apply Genetic Algorithms (GAs) to train VQCs used as quantum classifiers. As shown in the experiments, the application of GAs results in accurate solutions obtained with a reduced number of queries to quantum devices.
Quantum Support Vector Machines (QSVM) is one of the most promising frameworks in quantum machine learning, yet their performance depends on the design of the feature map. Conventional approaches rely on fixed quantum circuits, which often fail to generalize across datasets. To address this limitation, we propose GA-QSVM, a hybrid framework that employs Genetic Algorithms (GA) to automatically optimize feature maps. The proposed method introduces a configurable framework that flexibly defines the evolutionary parameters, enabling the construction of adaptive circuits. Experimental evaluation of datasets, including Digits, Fashion, Wine, and Breast Cancer, demonstrates that GA-QSVMs achieve a comparable accuracy compared to classical SVMs and standard QSVMs. Furthermore, transfer learning results indicate that GA-QSVM's circuits generalize effectively across datasets. These findings highlight the potential of evolutionary strategies to automate and enhance kernel design for future quantum machine learning applications.
Post-Variational Quantum Neural Networks (PVQNNs) eliminate the need for quantum parameter optimization by employing fixed, non-parametric circuits and shifting all learning to the classical domain, thereby avoiding barren plateaus entirely. However, the performance of PV-QNNs critically depends on the choice of ansatz architecture. In this work, we propose an automatic ansatz optimization framework based on Genetic Algorithms (GAs) that searches for optimal circuit ensembles under discrete constraints of topology, depth, and gate types. Quantum features are extracted efficiently via classical shadows and processed by a shallow classical classifier, preserving the post-variational property. Experimental results on the Fashion-MNIST (0 vs 4) classification task demonstrate that GA-selected ensembles outperform all baselines, achieving 89.0 % accuracy with 8 qubits, exceeding random and manually designed templates by3−5%\mathbf{3}-\mathbf{5} {\%}3−5%. Moreover, GA ensembles achieve up to1.9×1.9 \times1.9×higher accuracy per circuit and2×2 \times2×higher accuracy per twoqubit gate, while maintaining shallower circuits (6.0 depth vs. 8.6). These results indicate that evolutionary search effectively identifies compact, information-rich quantum circuits, enabling scalable and resource-efficient post-variational learning.
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This paper explores the application of genetic algorithms (GAs) to optimize quantum circuits. The core claim is that GA can effectively improve the efficiency and accuracy of quantum computation by strategically modifying the topology and control parameters of quantum circuits. The proposed mechanism treats a quantum circuit as a genetic individual, leveraging the principles of genetic algorithms – including crossover, mutation, and selection – to iteratively enhance circuit performance. This approach represents a novel method for quantum circuit optimization, offering a potentially robust and adaptable solution compared to traditional optimization techniques. We present a framework for implementing this approach, focusing on the algorithmic steps and considerations for successful application. The exploration will cover the challenges and potential benefits of integrating evolutionary algorithms with the unique characteristics of quantum computation. ---
In recent years, quantum machine learning has seen a substantial increase in the use of variational quantum circuits (VQCs). VQCs are inspired by artificial neural networks, which achieve extraordinary performance in a wide range of AI tasks as massively parameterized function approximators. VQCs have already demonstrated promising results, for example, in generalization and the requirement for fewer parameters to train, by utilizing the more robust algorithmic toolbox available in quantum computing. A VQCs' trainable parameters or weights are usually used as angles in rotational gates and current gradient-based training methods do not account for that. We introduce weight re-mapping for VQCs, to unambiguously map the weights to an interval of length 2π2π2π, drawing inspiration from traditional ML, where data rescaling, or normalization techniques have demonstrated tremendous benefits in many circumstances. We employ a set of five functions and evaluate them on the Iris and Wine datasets using variational classifiers as an example. Our experiments show that weight re-mapping can improve convergence in all tested settings. Additionally, we were able to demonstrate that weight re-mapping increased test accuracy for the Wine dataset by 10%10\%10% over using unmodified weights.