Data clustering on hybrid classical-quantum NISQ architecture with generative-based variational and parallel algorithms
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| Title: | Data clustering on hybrid classical-quantum NISQ architecture with generative-based variational and parallel algorithms |
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| Authors: | Rauch, Julien, Rontani, Damien, Vialle, Stéphane |
| Contributors: | Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay, Systèmes Parallèles - LISN (ParSys), Algorithmes, Apprentissage et Calcul (AAC), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire Matériaux Optiques, Photonique et Systèmes (LMOPS), CentraleSupélec-Université de Lorraine (UL), CentraleSupélec, CentraleSupélec campus de Metz, Université de Lorraine (UL), Quantum Paris-Saclay, Chair in Photonics, ANR-22-CMAS-0001,QuanTEdu-France,Quantum technologies: Education and training to fulfill the strategic skill needs of research and industry in France(2022) |
| Source: | ISSN: 1383-7621 ; Journal of Systems Architecture ; https://hal.science/hal-05040633 ; Journal of Systems Architecture, In press, Special Issue on Architecture of Computing Systems Conference 2024, 165, pp.103431. ⟨10.1016/j.sysarc.2025.103431⟩. |
| Publisher Information: | CCSD Elsevier |
| Publication Year: | 2025 |
| Subject Terms: | CPU-QPU computing, Data clustering, Quantum algorithms, QCBM, NISQ architectures, Noise impact, [INFO]Computer Science [cs] |
| Description: | International audience ; Clustering is a well-established unsupervised machine-learning approach to classify data automatically. In large datasets, the classical version of such algorithms performs well only if significant computing resources are available (e.g., GPU). An different approach relies on integrating a quantum processing unit (QPU) to alleviate the computing cost. This is achieved through the QPU’s ability to exploit quantum effects, such as superposition and entanglement, to natively parallelize computation or approximate multidimensional distributions for probabilistic computing (Born rule).In this paper, we propose first a clustering algorithm adapted to a hybrid CPU-QPU architecture while considering the current limitations of noisy intermediate-scale quantum (NISQ) technology. Secondly, we propose a quantum algorithm that exploits the probabilistic nature of quantum physics to make the most of our QPU’s potential. Our approach leverage on ideas from generative machine-learning algorithm and variational quantum algorithms (VQA) to design an hybrid QPU-CPU algorithm based on a mixture of so-called quantum circuits Born machines (QCBM). We implemented and tested the quality of our algorithm on an IBM quantum machine, then parallelized it to make better use of quantum resources and speed up the execution of quantum-based clustering algorithms.Finally, summarize the lessons learned from exploiting a CPU-QPU architecture on NISQ for data clustering. |
| Document Type: | article in journal/newspaper |
| Language: | English |
| DOI: | 10.1016/j.sysarc.2025.103431 |
| Availability: | https://hal.science/hal-05040633 https://doi.org/10.1016/j.sysarc.2025.103431 |
| Accession Number: | edsbas.18B1F4A9 |
| Database: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | International audience ; Clustering is a well-established unsupervised machine-learning approach to classify data automatically. In large datasets, the classical version of such algorithms performs well only if significant computing resources are available (e.g., GPU). An different approach relies on integrating a quantum processing unit (QPU) to alleviate the computing cost. This is achieved through the QPU’s ability to exploit quantum effects, such as superposition and entanglement, to natively parallelize computation or approximate multidimensional distributions for probabilistic computing (Born rule).In this paper, we propose first a clustering algorithm adapted to a hybrid CPU-QPU architecture while considering the current limitations of noisy intermediate-scale quantum (NISQ) technology. Secondly, we propose a quantum algorithm that exploits the probabilistic nature of quantum physics to make the most of our QPU’s potential. Our approach leverage on ideas from generative machine-learning algorithm and variational quantum algorithms (VQA) to design an hybrid QPU-CPU algorithm based on a mixture of so-called quantum circuits Born machines (QCBM). We implemented and tested the quality of our algorithm on an IBM quantum machine, then parallelized it to make better use of quantum resources and speed up the execution of quantum-based clustering algorithms.Finally, summarize the lessons learned from exploiting a CPU-QPU architecture on NISQ for data clustering. |
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| DOI: | 10.1016/j.sysarc.2025.103431 |