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Analysis of a hybrid quantum network for classification tasks

Gerhard Hellstern

Abstract In the era of noisy intermediate scaled quantum computers, one of the possible applications to search for an advantage of quantum computing is machine learning. Here, we report about an analysis, where a hybrid quantum‐classical network is applied to classify non‐trivial datasets (finance and MNIST data). In comparison to a pure classical network, we find an advantage when looking at several performance measures. As in classical machine learning, problems around overfitting the dataset arise. Therefore, we explore different possibilities to regularise the network.Abstract In the era of noisy intermediate scaled quantum computers, one of the possible applications to search for an advantage of quantum computing is machine learning. Here, we report about an analysis, where a hybrid quantum‐classical network is applied to classify non‐trivial datasets (finance and MNIST data). In comparison to a pure classical network, we find an advantage when looking at several performance measures. As in classical machine learning, problems around overfitting the dataset arise. Therefore, we explore different possibilities to regularise the network.

IET Quantum CommunicationAug 14, 20217 citations

A Comparative Analysis of Hybrid-Quantum Classical Neural Networks

Kamila Zaman, Tasnim Ahmed, Muhammad Abdullah Hanif +2 more

arXiv:2402.10540Communications in computer and information scienceJan 1, 202528 citations

Comparative Study of Quantum and Classical Layers in Hybrid Quantum Neural Networks

Aakarsh Etar, Sarvapriya M. Tripathi, Jayesh Soni +2 more

Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications EngineeringJul 16, 20260 citations

Quantum neural networks for power flow analysis

Zeynab Kaseb, Matthias Möller, Giorgio Tosti Balducci +2 more

This paper explores the potential application of quantum and hybrid quantum–classical neural networks in power flow analysis. Experiments are conducted using two datasets based on 4-bus and 33-bus test systems. A systematic performance comparison is also conducted among quantum, hybrid quantum–classical, and classical neural networks. The comparison is based on (i) generalization ability, (ii) robustness, (iii) training dataset size needed, (iv) training error, and (v) training process stability. The results show that the developed hybrid quantum–classical neural network outperforms both quantum and classical neural networks, and hence can improve deep learning-based power flow analysis in the noisy-intermediate-scale quantum (NISQ) and fault-tolerant quantum (FTQ) era.This paper explores the potential application of quantum and hybrid quantum–classical neural networks in power flow analysis. Experiments are conducted using two datasets based on 4-bus and 33-bus test systems. A systematic performance comparison is also conducted among quantum, hybrid quantum–classical, and classical neural networks. The comparison is based on (i) generalization ability, (ii) robustness, (iii) training dataset size needed, (iv) training error, and (v) training process stability. The results show that the developed hybrid quantum–classical neural network outperforms both quantum and classical neural networks, and hence can improve deep learning-based power flow analysis in the noisy-intermediate-scale quantum (NISQ) and fault-tolerant quantum (FTQ) era.

arXiv:2311.06293Electric Power Systems ResearchJul 3, 202427 citations

Quantum assisted unsupervised data clustering on the basis of neural networks

I. D. Lazarev, Marek Narożniak, Tim Byrnes +1 more

Recent progress in machine learning has affected almost all areas of the modern economy. The use of quantum protocols to speed up classical machine learning approaches may have further profound effects on society in the future. Here, we developed a hybrid quantum-assisted self-organizing feature map, a type of artificial neural network, and apply it to the data clustering problem in an unsupervised manner. We show that it allows us to reduce the number of calculations in a number of clusters. It is believed that similar types of hybrid quantum classical algorithms can be the main test bed to achieve practical quantum supremacy on Noisy Intermediate Scale Quantum devices.Recent progress in machine learning has affected almost all areas of the modern economy. The use of quantum protocols to speed up classical machine learning approaches may have further profound effects on society in the future. Here, we developed a hybrid quantum-assisted self-organizing feature map, a type of artificial neural network, and apply it to the data clustering problem in an unsupervised manner. We show that it allows us to reduce the number of calculations in a number of clusters. It is believed that similar types of hybrid quantum classical algorithms can be the main test bed to achieve practical quantum supremacy on Noisy Intermediate Scale Quantum devices.

International Conference on Micro- and Nano-Electronics (ICMNE)Jan 31, 20221 citation

Studying the Impact of Quantum-Specific Hyperparameters on Hybrid Quantum-Classical Neural Networks

Kamila Zaman, Tasnim Ahmed, Muhammad Kashif +3 more

arXiv:2402.10605Communications in computer and information scienceJan 1, 202513 citations

Analysis of a Hybrid Quantum-Classical Neural Network for Noisy Intermediate-Scale Quantum Machine Learning Problems

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