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Data clustering on hybrid classical-quantum NISQ architecture with generative-based variational and parallel algorithms

Julien Rauch, Damien Rontani, Stéphane Vialle

Journal of Systems ArchitectureMay 6, 20254 citations

Towards Hybrid Quantum-Classical Deep Learning Architecture for Indoor-Outdoor Detection Using QCNN-LSTM and Cluster State Signal Processing

Muhammad Bilal Akram Dastagir, Dongsoo Han

Quantum computing, combined with deep learning, leverages principles like superposition and entanglement to enhance complex data-driven tasks. The Noisy Intermediate-Scale Quantum (NISQ) era presents opportunities for hybrid quantum-classical architectures to address this challenge. Despite significant progress, practical applications of these hybrid models are limited. This letter proposes a novel hybrid quantum-classical deep learning architecture, integrating Quantum Convolutional Neural Networks (QCNNs) and Long-Short-Term Memory (LSTM) networks, enhanced by Cluster State Signal Processing. Furthermore, this letter addresses indoor-outdoor detection using high-dimensional signal data, utilizing the Cirq platform—a Python framework for developing and simulating Noisy Intermediate Scale Quantum (NISQ) circuits on quantum computers and simulators. The approach addresses noise and decoherence issues. Preliminary results show that the QCNN-LSTM model outperforms pure quantum and hybrid models in accuracy and efficiency. This validates the practical benefits of hybrid architectures, paving the way for advancements in complex data classification like indoor-outdoor detection.Quantum computing, combined with deep learning, leverages principles like superposition and entanglement to enhance complex data-driven tasks. The Noisy Intermediate-Scale Quantum (NISQ) era presents opportunities for hybrid quantum-classical architectures to address this challenge. Despite significant progress, practical applications of these hybrid models are limited. This letter proposes a novel hybrid quantum-classical deep learning architecture, integrating Quantum Convolutional Neural Networks (QCNNs) and Long-Short-Term Memory (LSTM) networks, enhanced by Cluster State Signal Processing. Furthermore, this letter addresses indoor-outdoor detection using high-dimensional signal data, utilizing the Cirq platform—a Python framework for developing and simulating Noisy Intermediate Scale Quantum (NISQ) circuits on quantum computers and simulators. The approach addresses noise and decoherence issues. Preliminary results show that the QCNN-LSTM model outperforms pure quantum and hybrid models in accuracy and efficiency. This validates the practical benefits of hybrid architectures, paving the way for advancements in complex data classification like indoor-outdoor detection.

IEEE Signal Processing LettersJan 1, 20249 citations

NISQ-friendly measurement-based quantum clustering algorithms

Srushti Patil, Shreya Banerjee, Prasanta Kumar Panigrahi

arXiv:2302.00566Quantum Information ProcessingOct 8, 20245 citations

Efficient Quantum Algorithms for Data Clustering

Julien Rauch

Data clustering consists of grouping data without prior training, a process known as unsupervised learning. Given the constant growth of database volumes, contemporary computers require significant computational time to perform this partitioning. To effectively address this challenge, quantum computing offers a promising perspective, as it could eventually provide more efficient resources for clustering massive datasets. However, current quantum computers are not yet mature; they are prone to noise, which limits their practical utility. In this context, we have designed a hybrid classical-quantum clustering algorithm tailored for the current Noisy Intermediate-Scale Quantum (NISQ) era. We evaluated this algorithm under various conditions and across different types of datasets, comparing it against several well-known clustering algorithms. Our results demonstrate that, under certain conditions, quantum noise can actually provide an advantage to our algorithm. Furthermore, we worked on deploying this algorithm on actual quantum processors and developed tools to optimize this deployment. Finally, to diversify the quantum architectures used, we extended our research to include neutral-atom quantum computers.Data clustering consists of grouping data without prior training, a process known as unsupervised learning. Given the constant growth of database volumes, contemporary computers require significant computational time to perform this partitioning. To effectively address this challenge, quantum computing offers a promising perspective, as it could eventually provide more efficient resources for clustering massive datasets. However, current quantum computers are not yet mature; they are prone to noise, which limits their practical utility. In this context, we have designed a hybrid classical-quantum clustering algorithm tailored for the current Noisy Intermediate-Scale Quantum (NISQ) era. We evaluated this algorithm under various conditions and across different types of datasets, comparing it against several well-known clustering algorithms. Our results demonstrate that, under certain conditions, quantum noise can actually provide an advantage to our algorithm. Furthermore, we worked on deploying this algorithm on actual quantum processors and developed tools to optimize this deployment. Finally, to diversify the quantum architectures used, we extended our research to include neutral-atom quantum computers.

Oct 8, 20260 citations

Hybrid quantum-classical framework for clustering

Antonin Lefevre

Elsevier eBooksJan 1, 20250 citations

Advancing quantum-classical hybrid workflows : algorithm selection, circuit generation, and optimization for NISQ processors (要旨)

マオ, イーカイ, Mao, Yikai, 毛, 翊愷

Jan 1, 20250 citations

Generative-Based Algorithm for Data Clustering on Hybrid Classical-Quantum NISQ Architecture

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