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Lecture notes in computer science

Multi-objective Quantum-Inspired Genetic Algorithm for Supervised Learning of Deep Classification Models

Jerzy Balicki

Published Jan 1, 2023• 2 citations
Publisher
DOI
10.1007/978-3-031-36030-5_20

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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.
Abstract There have been numerous quantum neural networks reported, but they struggle to match traditional neural networks in accuracy. Given the huge improvement of the neural network models’ accuracy by two-dimensional tensor network (TN) states in classical tensor network machine learning (TNML), it is promising to explore whether its application in quantum machine learning can extend the performance boundary of the models. Here, we transform two-dimensional TNs into quantum circuits for supervised learning. Specifically, we encode two-dimensional TNs into quantum circuits through rigorous mathematical proofs for constructing model ansätze, including string-bond states, entangled-plaquette states and isometric TN states. In addition, we propose adaptive data encoding methods and combine with TNs. We construct a tensor-network-inspired quantum circuit (TNQC) supervised learning framework for transferring TNML from classical to quantum, and build several novel two-dimensional TN-inspired quantum classifiers based on this framework. Finally, we propose a parallel quantum machine learning method for multi-class classification to construct 2D TNQC-based multi-class classifiers. Classical simulation results on the MNIST benchmark dataset show that our proposed models achieve the state-of-the-art accuracy performance, significantly outperforming other quantum classifiers on both binary and multi-class classification tasks, and beat simple convolutional classifiers on a fair track with identical inputs. The noise resilience of the models makes them successfully run and work in a real quantum computer.