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Evolutionary-Based Quantum Classification for Surface Electromyography Signals

Giovanni Acampora, Angela Chiatto, Autilia Vitiello

Published Mar 17, 2025• 1 citation
Publisher

Abstract

The classification of surface electromyography (sEMG) signals represents a key objective in the domain of rehabilitation and prosthetic control. This task is particularly challenging due to several factors, including the high dimensionality of the data and the high computational cost of the currently used deep learning models. In this context, quantum computing methods may prove advantageous due to their capacity to process a substantial amount of data facilitated by quantum phenomena such as superposition. In light of these considerations, the objective of this study is to demonstrate the feasibility of quantum computing by performing a comparison between evolutionarybased quantum classifiers for sEMG signals. In particular, the considered quantum classifiers are based on variational quantum circuits trained using evolutionary algorithms such as genetic algorithms, differential evolution and particle swarm optimization. As demonstrated in the experimental session utilizing a wellknown hand gesture dataset, quantum classifiers, and especially those based on genetic algorithms, are effective for sEMG signal classification.

DOI
10.1109/mciicompanion65207.2025.11007472

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The classification of surface electromyography (sEMG) signals represents a key objective in the domain of rehabilitation and prosthetic control. This task is particularly challenging due to several factors, including the high dimensionality of the data and the high computational cost of the currently used deep learning models. In this context, quantum computing methods may prove advantageous due to their capacity to process a substantial amount of data facilitated by quantum phenomena such as superposition. In light of these considerations, the objective of this study is to demonstrate the feasibility of quantum computing by performing a comparison between evolutionarybased quantum classifiers for sEMG signals. In particular, the considered quantum classifiers are based on variational quantum circuits trained using evolutionary algorithms such as genetic algorithms, differential evolution and particle swarm optimization. As demonstrated in the experimental session utilizing a wellknown hand gesture dataset, quantum classifiers, and especially those based on genetic algorithms, are effective for sEMG signal classification.
Variational Quantum Classifiers (VQCs) stand out as one of the most popular classification approaches within the Quantum Machine Learning (QML) community. They serve as an accessible entry point for newcomers to QML world, particularly those with a background in classical machine learning, due to their workflow similarity to conventional neural networks. In essence, VQCs boast a layered structure conducive to training via a classical optimizer. Recently, Evolutionary Algorithms (EAs) have emerged as a suitable methodology for this training goal. However, current quantum tools provide implementations for various gradient-based optimizers and just one EA tailored for VQC training. Moreover, it is important to note that access to this toolset remains largely confined to expert users. This highlights the necessity for platforms that facilitate the utilization of EAs for VQC training by non-experts in quantum or evolutionary computing. This work introduces a web application designed to implement quantum classifiers to be trained on user-supplied datasets by means of a set of EAs, which can be customized by users. The capabilities of the proposed web application are shown in the experimental session, where a VQC is trained to classify a well-known dataset using a variety of EAs.
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.
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.
With the advent of the era of big data, how to efficiently process data has become an urgent problem. Although classical machine learning algorithms are very mature in processing data, they require a large amount of data to complete the training process. Quantum machine learning uses the superposition, entanglement, parallelism and other characteristics of quantum computing to improve the performance of classical machine learning algorithms. At the same time, circuit optimization of the quantum classifier can reduce the computational complexity. This paper proposes an automatic optimization model of the classifier based on the variational quantum algorithm, which reduces the complexity of the parameter update process through the deep optimization of the core circuit. This model greatly speeds up the training process and has lower complexity than other quantum classifiers. In addition, a unique regularization method is used to avoid overfitting. The simulation results show that the classification of some typical data sets can achieve better classification results, and the training parameters and training speed are significantly better than other quantum classifiers. Finally, the robustness of the classifier is further verified in the presence of noise.
Quantum supervised learning, utilizing variational circuits, stands out as a promising technology for NISQ devices due to its efficiency in hardware resource utilization during the creation of quantum feature maps and the implementation of hardware-efficient ansatz with trainable parameters. Despite these advantages, the training of quantum models encounters challenges, notably the barren plateau phenomenon, leading to stagnation in learning during optimization iterations. This study proposes an innovative approach: an evolutionary-enhanced ansatz-free supervised learning model. In contrast to parametrized circuits, our model employs circuits with variable topology that evolves through an elitist method, mitigating the barren plateau issue. Additionally, we introduce a novel concept, the superposition of multi-hot encodings, facilitating the treatment of multi-classification problems. Our framework successfully avoids barren plateaus, resulting in enhanced model accuracy. Comparative analysis with variational quantum classifiers from the technology's state-of-the-art reveal a substantial improvement in training efficiency and precision. Furthermore, we conduct tests on a challenging dataset class, traditionally problematic for conventional kernel machines, demonstrating a potential alternative path for achieving quantum advantage in supervised learning for NISQ era.
Quantum Neural Networks (QNNs) are a promising tool for addressing the limitations of their classical counterparts in many applications, including healthcare. However, most existing approaches rely on gradient-based optimization methods, which require a large number of quantum circuit calls and are affected by barren plateaus, limiting their applicability on current Noisy Intermediate-Scale Quantum (NISQ) devices. This work investigates Evolutionary Quantum Neural Networks (EQNNs) for heart disease classification using the Cleveland dataset. The proposed framework replaces gradient-based training with a Genetic Algorithm (GA), enabling parameter optimization without explicit gradient computation and reducing the dependence of training cost on the number of circuit parameters. Experimental results compare the GA with the ADAM optimizer across different circuit sizes and under both ideal and noisy execution conditions using a mock IBM backend. The results demonstrate that EQNNs achieve competitive classification performance while significantly reducing the number of required quantum circuit evaluations, highlighting their potential as a resource-efficient solution for medical diagnosis on NISQ devices.