Development of quantum neural networks for complex data classification
DOI:
https://doi.org/10.65126/jocosir.v1i4.32Keywords:
Complex Data Classification, Machine Learning, Quantum Computing, Quantum Error Correction, Quantum Neural NetworksAbstract
This research explores the development of Quantum Neural Networks (QNNs) as a transformative approach for complex data classification. Utilizing a numerical example, we illustrate the foundational quantum principles of superposition and entanglement within QNNs. The hybrid quantum-classical processing paradigm is introduced, emphasizing the seamless integration of quantum and classical components, acknowledging the challenges of quantum error correction and noise in Noisy Intermediate-Scale Quantum (NISQ) devices. While the example is deliberately simple, it serves as a starting point for understanding the unique advantages and challenges associated with QNNs. Our findings highlight the potential of quantum computation for parallel processing but also underscore the need to address current limitations for practical applications. Future research directions include investigating sophisticated quantum circuits, exploring error mitigation strategies, and assessing QNN performance across diverse datasets. Collaboration between quantum computing and machine learning communities is essential for the advancement of QNNs, and developments in quantum hardware will play a pivotal role in realizing their full potential. This study contributes to the evolving discourse at the intersection of quantum computing and machine learning, providing foundational insights and laying the groundwork for further exploration in this rapidly advancing field.
References
H. He, Y. Wang, Y. Qi, Z. Xu, Y. Li, and Y. Wang, “From Prediction to Design: Recent Advances in Machine Learning for the Study of 2D Materials,” Nano Energy, p. 108965, 2023.
K. P. BH, “A novel approach for efficient data partitioning to balance computation and minimize data shuffling,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 11s, pp. 368–381, 2023.
F. Karimzadeh, M. Imani, B. Asgari, N. Cao, Y. Lin, and Y. Fang, “Memory-Based Computing for Energy-Efficient AI: Grand Challenges,” in 2023 IFIP/IEEE 31st International Conference on Very Large Scale Integration (VLSI-SoC), IEEE, 2023, pp. 1–8.
S. Pal, “Quantum Computing And Algorithms: An Exploration Of The Quantum Frontier In Data Analytics And Computational Intelligence,” Juni Khyat, vol. 13, no. 10, pp. 145–154, 2023.
M. Avramouli, I. K. Savvas, A. Vasilaki, and G. Garani, “Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery,” Electronics, vol. 12, no. 11, p. 2402, 2023.
M. V Altaisky and N. E. Kaputkina, “Quantum Neural Networks and Quantum Intelligence,” Rhythm. Oscil. Proteins to Hum. Cogn., pp. 165–185, 2021.
A. Safari and A. A. Ghavifekr, “Quantum Neural Networks (QNN) Application in Weather Prediction of Smart Grids,” in 2021 11th Smart Grid Conference (SGC), IEEE, 2021, pp. 1–6.
N. C. Thompson, K. Greenewald, K. Lee, and G. F. Manso, “The computational limits of deep learning,” arXiv Prepr. arXiv2007.05558, 2020.
S. Mangini, F. Tacchino, D. Gerace, D. Bajoni, and C. Macchiavello, “Quantum computing models for artificial neural networks,” Europhys. Lett., vol. 134, no. 1, p. 10002, 2021.
R. Kharsa, A. Bouridane, and A. Amira, “Advances in Quantum Machine Learning and Deep Learning for Image Classification: A Survey,” Neurocomputing, vol. 560, p. 126843, 2023.
Z. Abohashima, M. Elhosen, E. H. Houssein, and W. M. Mohamed, “Classification with quantum machine learning: A survey,” arXiv Prepr. arXiv2006.12270, 2020.
U. Lizarralde Imaz, “Building hybrid classical-quantum classifiers to deal with unbalanced datasets,” 2022.
L. Huynh, J. Hong, A. Mian, H. Suzuki, Y. Wu, and S. Camtepe, “Quantum-Inspired Machine Learning: a Survey,” arXiv Prepr. arXiv2308.11269, 2023.
D. Snell, “‘Just transition’? Conceptual challenges meet stark reality in a ‘transitioning’coal region in Australia,” Globalizations, vol. 15, no. 4, pp. 550–564, 2018.
Y. Suzuki, S. Endo, K. Fujii, and Y. Tokunaga, “Quantum error mitigation as a universal error reduction technique: Applications from the nisq to the fault-tolerant quantum computing eras,” PRX Quantum, vol. 3, no. 1, p. 10345, 2022.
J. Liu and H. Zhou, “Reliability modeling of nisq-era quantum computers,” in 2020 IEEE international symposium on workload characterization (IISWC), IEEE, 2020, pp. 94–105.
M. Kordzanganeh et al., “Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms,” Adv. Quantum Technol., vol. 6, no. 8, p. 2300043, 2023.
F. Yan, S. E. Venegas-Andraca, and K. Hirota, “Toward implementing efficient image processing algorithms on quantum computers,” Soft Comput., vol. 27, no. 18, pp. 13115–13127, 2023.
D. Iqbal, “A Deep Dive into Neural Networks: Architectures, Training Techniques, and Practical Implementations,” J. Environ. Sci. Technol., vol. 2, no. 2, pp. 61–71, 2023.
M. Jin et al., “A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection,” arXiv Prepr. arXiv2307.03759, 2023.
F. Stoppa et al., “AutoSourceID-Classifier-Star-galaxy classification using a convolutional neural network with spatial information,” Astron. Astrophys., vol. 680, p. A109, 2023.
C. Ciliberto et al., “Quantum machine learning: a classical perspective,” Proc. R. Soc. A Math. Phys. Eng. Sci., vol. 474, no. 2209, p. 20170551, 2018.
V. Dunjko and H. J. Briegel, “Machine learning & artificial intelligence in the quantum domain: a review of recent progress,” Reports Prog. Phys., vol. 81, no. 7, p. 74001, 2018.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,” Nature, vol. 549, no. 7671, pp. 195–202, 2017.
M. Schuld, I. Sinayskiy, and F. Petruccione, “The quest for a quantum neural network,” Quantum Inf. Process., vol. 13, pp. 2567–2586, 2014.
N. Killoran, T. R. Bromley, J. M. Arrazola, M. Schuld, N. Quesada, and S. Lloyd, “Continuous-variable quantum neural networks,” Phys. Rev. Res., vol. 1, no. 3, p. 33063, 2019.
K. H. Wan, O. Dahlsten, H. Kristjánsson, R. Gardner, and M. S. Kim, “Quantum generalisation of feedforward neural networks,” npj Quantum Inf., vol. 3, no. 1, p. 36, 2017.
Y. Li, R.-G. Zhou, R. Xu, J. Luo, and W. Hu, “A quantum deep convolutional neural network for image recognition,” Quantum Sci. Technol., vol. 5, no. 4, p. 44003, 2020.
Y. Takaki, K. Mitarai, M. Negoro, K. Fujii, and M. Kitagawa, “Learning temporal data with a variational quantum recurrent neural network,” Phys. Rev. A, vol. 103, no. 5, p. 52414, 2021.
A. Perdomo-Ortiz, M. Benedetti, J. Realpe-Gómez, and R. Biswas, “Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers,” Quantum Sci. Technol., vol. 3, no. 3, p. 30502, 2018.
M. A. Metawei, H. Said, M. Taher, H. Eldeib, and S. M. Nassar, “Survey on hybrid classical-quantum machine learning models,” in 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), IEEE, 2020, pp. 1–6.
V. Dunjko, J. M. Taylor, and H. J. Briegel, “Quantum-enhanced machine learning,” Phys. Rev. Lett., vol. 117, no. 13, p. 130501, 2016.
V. Havlíček et al., “Supervised learning with quantum-enhanced feature spaces,” Nature, vol. 567, no. 7747, pp. 209–212, 2019.
T. Hur, L. Kim, and D. K. Park, “Quantum convolutional neural network for classical data classification,” Quantum Mach. Intell., vol. 4, no. 1, p. 3, 2022.
M. Benedetti, D. Garcia-Pintos, O. Perdomo, V. Leyton-Ortega, Y. Nam, and A. Perdomo-Ortiz, “A generative modeling approach for benchmarking and training shallow quantum circuits,” npj Quantum Inf., vol. 5, no. 1, p. 45, 2019.
F. V. Massoli, L. Vadicamo, G. Amato, and F. Falchi, “A leap among quantum computing and quantum neural networks: A survey,” ACM Comput. Surv., vol. 55, no. 5, pp. 1–37, 2022.
K. Bharti et al., “Noisy intermediate-scale quantum algorithms,” Rev. Mod. Phys., vol. 94, no. 1, p. 15004, 2022.
R. Zhao and S. Wang, “A review of quantum neural networks: methods, models, dilemma,” arXiv Prepr. arXiv2109.01840, 2021.
J. Shi et al., “Two end-to-end quantum-inspired deep neural networks for text classification,” IEEE Trans. Knowl. Data Eng., 2021.
N. Liu and P. Rebentrost, “Quantum machine learning for quantum anomaly detection,” Phys. Rev. A, vol. 97, no. 4, p. 42315, 2018.
P. Rebentrost, M. Mohseni, and S. Lloyd, “Quantum support vector machine for big data classification,” Phys. Rev. Lett., vol. 113, no. 13, p. 130503, 2014.
S. Gupta and R. K. P. Zia, “Quantum neural networks,” J. Comput. Syst. Sci., vol. 63, no. 3, pp. 355–383, 2001.
S. T. Marella and H. S. K. Parisa, “Introduction to quantum computing,” Quantum Comput. Commun., 2020.
I. Siddiqi, “Engineering high-coherence superconducting qubits,” Nat. Rev. Mater., vol. 6, no. 10, pp. 875–891, 2021.
A. Blais, S. M. Girvin, and W. D. Oliver, “Quantum information processing and quantum optics with circuit quantum electrodynamics,” Nat. Phys., vol. 16, no. 3, pp. 247–256, 2020.
F. Tacchino, A. Chiesa, S. Carretta, and D. Gerace, “Quantum computers as universal quantum simulators: state‐of‐the‐art and perspectives,” Adv. Quantum Technol., vol. 3, no. 3, p. 1900052, 2020.
A. Strikis, D. Qin, Y. Chen, S. C. Benjamin, and Y. Li, “Learning-based quantum error mitigation,” PRX Quantum, vol. 2, no. 4, p. 40330, 2021.
R. Ramouthar and H. Seker, “Hybrid Quantum-Classical Computing-A Fusion of Classical And Quantum Computational Substrates,” 2023.
F. Fan, Y. Shi, T. Guggemos, and X. X. Zhu, “Hybrid quantum-classical convolutional neural network model for image classification,” IEEE Trans. Neural Networks Learn. Syst., 2023.
E. Ghasemian and M. K. Tavassoly, “Hybrid classical-quantum machine learning based on dissipative two-qubit channels,” Sci. Rep., vol. 12, no. 1, p. 20440, 2022.
T. S. Humble, A. McCaskey, D. I. Lyakh, M. Gowrishankar, A. Frisch, and T. Monz, “Quantum computers for high-performance computing,” IEEE Micro, vol. 41, no. 5, pp. 15–23, 2021.
R. Ramouthar and H. Seker, “A Review of Hybrid Quantum Computational Substrates-A Fusion of Classical And Quantum Computational Substrates,” Sci. Prepr., 2023.
V. E. Elfving et al., “How will quantum computers provide an industrially relevant computational advantage in quantum chemistry?,” arXiv Prepr. arXiv2009.12472, 2020.
M. Ostaszewski, L. M. Trenkwalder, W. Masarczyk, E. Scerri, and V. Dunjko, “Reinforcement learning for optimization of variational quantum circuit architectures,” Adv. Neural Inf. Process. Syst., vol. 34, pp. 18182–18194, 2021.
N. Nguyen and K.-C. Chen, “Quantum embedding search for quantum machine learning,” IEEE Access, vol. 10, pp. 41444–41456, 2022.
A. D. Córcoles et al., “Exploiting dynamic quantum circuits in a quantum algorithm with superconducting qubits,” Phys. Rev. Lett., vol. 127, no. 10, p. 100501, 2021.
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Copyright (c) 2023 Asgari Savvas, Mian Snell Lizarralde, Patrisia Teresa Marsoit
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