Quantum Perceptron: A Novel Approach to Predicting Unemployment Levels in North Sumatra Province

  • Solikhun STIKOM Tunas Bangsa
  • Dimas Trianda STIKOM Tunas Bangsa
Keywords: Analysis, quantum perceptron, quantum computing, initial prediction

Abstract

The application of Quantum Computing to improve the perceptron algorithm in unemployment prediction is a new aspect of this research. This study focuses on unemployment, which is a big challenge for the young generation in Indonesia, especially in the North Sumatra region. This research applies the quantum perceptron method to provide an alternative solution in predicting the unemployment rate. The data used in this analysis comes from the North Sumatra Central Statistics Agency and includes published unemployment rates (TPT) for individuals aged 15 years and over from 2017 to 2023. This research uses seven variables ranging from x1 to x7 to produce accurate data. Quantum perceptron methods offer specific advantages over traditional methods, including higher computing speeds and the ability to handle greater data complexity. This analysis aims to identify unemployment patterns and trends in North Sumatra and provide more accurate predictions by applying the quantum perceptron method. Although the results of this research are still limited to analysis, this research shows promising results and opens up opportunities for further, more in-depth research. This research is limited to predicting unemployment rates in North Sumatra. The use of quantum computing using the quantum perceptron method shows great potential for application to various other socio-economic problems in the future. This research contributes by introducing a new approach that utilizes quantum technology to improve prediction accuracy in economic analysis.

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References

N. Wiebe, A. Kapoor, and K. M. Svore, “Quantum perceptron models,” Adv. Neural Inf. Process. Syst., no. Nips, pp. 4006–4014, 2016, doi: https://doi.org/10.48550/arXiv.1602.04799.

A. Zlokapa and A. Gheorghiu, “A deep learning model for noise prediction on near-term quantum devices,” pp. 1–5, doi: https://doi.org/10.48550/arXiv.2005.10811.

T. Baidawi and Solikhun, “Comparison of Madaline and Perceptron Algorithms on Classification with Quantum Computing Approach,” vol. 5, no. 158, pp. 280–287, 2024, doi: https://doi.org/10.29207/resti.v8i2.5502.

Marlini Septi, “Kebijakan Pemerintah Indonesia Dalam Mengatasi Masalah Pengangguran Akibat Pandemi Covid 19,” Int. Conf. Teach. English Lit., vol. 1, no. 1, pp. 46–50, 2020.

M. Roget, G. Di Molfetta, É. Normale, and S. De Lyon, “Quantum Perceptron Revisited : Computational-Statistical Tradeoffs,” no. Ml, pp. 1697–1706, 2022.

E. Torrontegui and C. Wunderlich, “Realization of a quantum perceptron gate with trapped ions,” pp. 1–5.

Y. Teguh, A. Fikri, and I. A. Gopar, “Analisis Peningkatan Angka Pengangguran akibat Dampak Pandemi Covid 19 di Indonesia,” Indones. J. Bus. Anal., vol. 1, no. 2, pp. 107–116, 2021, doi: https://doi.org/10.55927/ijba.v1i2.19.

R. Parthasarathy and R. T. Bhowmik, “Quantum Optical Convolutional Neural Network : A Novel Image Recognition Framework for Quantum Computing,” IEEE Access, vol. 9, pp. 103337–103346, 2021, doi: 10.1109/ACCESS.2021.3098775.

D. Bokhan, A. S. Mastiukova, A. S. Boev, D. N. Trubnikov, and A. K. Fedorov, “Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning,” no. November, pp. 1–8, 2022, doi: 10.3389/fphy.2022.1069985.

S. Adiguno, Y. Syahra, and M. Yetri, “Prediksi Peningkatan Omset Penjualan Menggunakan Metode Regresi Linier Berganda,” J. Sist. Inf. Triguna Dharma (JURSI TGD), vol. 1, no. 4, p. 275, 2022, doi: 10.53513/jursi.v1i4.5331.

S. K. Joshi et al., “Advances in space quantum communications,” no. May, pp. 182–217, 2021, doi: 10.1049/qtc2.12015.

D. Herman et al., “Quantum computing for finance,” Nat. Rev. Phys., pp. 1–29, 2023, doi: https://doi.org/10.1038/s42254-023-00603-1.

K. Najafi, S. F. Yelin, and X. Gao, “The Development of Quantum Machine Learning,” Harvard Data Sci. Rev., no. 4, pp. 1–21, 2022, doi: 10.1162/99608f92.5a9fd72c.

L. M. Gultom, “Klasifikasi Data Dengan Quantum Perceptron,” Teknovasi, vol. 4, no. 1, pp. 1–9, 2017.

L. Marchetti et al., “Quantum computing algorithms: getting closer to critical problems in computational biology,” Brief. Bioinform., vol. 23, no. 6, pp. 1–15, 2022, doi: 10.1093/bib/bbac437.

K. Beer et al., “Training deep quantum neural networks,” Nat. Commun., vol. 11, no. 1, pp. 1–6, 2020, doi: 10.1038/s41467-020-14454-2.

J. Carrasco, A. Elben, C. Kokail, B. Kraus, and P. Zoller, “Theoretical and Experimental Perspectives of Quantum Verification,” Phys. Rev. Appl., vol. 10, no. 1, p. 1, 2021, doi: 10.1103/PRXQuantum.2.010102.

J. Logeshwaran, “The moment probability and impacts monitoring for electron cloud behavior of electronic computers by using quantum deep learning model,” Neuro Quantology, no. September, 2022, doi: 10.14704/nq.2022.20.8.NQ44634.

A. Thakur, “Fundamentals of Neural Networks,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 9, no. VIII, pp. 407–426, 2021, doi: 10.22214/ijraset.2021.37362.

S. Pradhana, H. Fitriyah, and M. H. H. Ichsan, “Sistem kendali kualitas air kolam ikan nila dengan metode jaringan syaraf tiruan berdasarkan pH dan turbidity berbasis arduino uno,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 10, pp. 4197–4204, 2021.

F. Rahmadani, A. M. H. Pardede, and Nurhayati, “Jaringan Syaraf Tiruan Prediksi Jumlah Pengiriman Barang Menggunakan Metode Backpropagation,” J. Tek. Inform. Kaputama, vol. 5, no. 1, pp. 100–106, 2021.

A. B. Untoro, “Prediksi Harga Saham Dengan Menggunakan Jaringan Syaraf Tiruan,” J. Teknol. Inform. dan Komput., vol. 6, no. 2, pp. 103–111, 2020, doi: 10.37012/jtik.v6i2.212.

W. Wirayudha, T. Hastono, and M. Mujahidin, “Introduction To Healthy Low-Sugar Foods Using Artificial Neural Networks With the Perceptron Method,” JTH J. Technol. Heal., vol. 1, no. 1, pp. 43–49, 2023, doi: 10.61677/jth.vi.6.

G. Zhou et al., “Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics,” PNAS, pp. 1–10, 2022, doi: 10.1073/pnas.2120333119/-/DCSupplemental.Published.

M. G. M. Abdolrasol et al., “Artificial neural networks based optimization techniques: A review,” Electron., vol. 10, no. 21, 2021, doi: 10.3390/electronics10212689.

S. Nosratabadi, S. F. Ardabili, Z. Lakner, C. Makó, and A. Mosavi, “Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS,” SSRN Electron. J., pp. 1–13, 2021, doi: 10.2139/ssrn.3836565.

M. Yanto, R. Sovia, and E. P. W. Mandala, “Jaringan Syaraf Tiruan Perceptron Untuk Penentuan Pola Sistem Irigasi Lahan Pertanian Di Kabupaten Pesisir Selatan Sumatra Barat,” Sebatik, vol. 22, no. 2, pp. 111–115, 2018, doi: 10.46984/sebatik.v22i2.317.

K. Sharma, M. Cerezo, L. Cincio, and P. J. Coles, “Trainability of Dissipative Perceptron-Based Quantum Neural Networks,” Phys. Rev. Lett., vol. 128, no. 18, 2022, doi: 10.1103/PhysRevLett.128.180505.

A. J. da Silva, T. B. Ludermir, and W. R. de Oliveira, “Quantum perceptron over a field and neural network architecture selection in a quantum computer,” Neural Networks, vol. 76, pp. 55–64, 2016, doi: 10.1016/j.neunet.2016.01.002.

A. Gratsea, V. Kasper, and M. Lewenstein, “Storage properties of a quantum perceptron,” pp. 1–13, 2021, doi: https://doi.org/10.48550/arXiv.2111.08414.

S. Solikhun and V. Yasin, “Analisis Quantum Perceptron Untuk Memprediksi Jumlah Pengunjung Ucok Kopi Pematangsiantar Pada Masa Pandemi Covid-19,” J. Edukasi dan Penelit. Inform., vol. 8, no. 1, p. 162, 2022, doi: 10.26418/jp.v8i1.52191.

N. Innan and M. Bennai, “Simulation of a Variational Quantum Perceptron using Grover’s Algorithm,” pp. 1–10, 2023, doi: https://doi.org/10.1088/1402-4896/ad3e38.

S. Garg and G. Ramakrishnan, “Advances in Quantum Deep Learning: An Overview,” 2020, doi: https://doi.org/10.48550/arXiv.2005.04316.

T. M. Khan and A. Robles-Kelly, “Machine Learning: Quantum vs Classical,” IEEE Access, vol. 8, pp. 219275–219294, 2020, doi: 10.1109/ACCESS.2020.3041719.

S. Mangini, F. Tacchino, D. Gerace, D. Bajoni, and C. Macchiavello, “Quantum computing models for artificial neural networks,” Epl, vol. 134, no. 1, 2021, doi: 10.1209/0295-5075/134/10002.

Published
2024-10-06
How to Cite
Solikhun, & Trianda, D. (2024). Quantum Perceptron: A Novel Approach to Predicting Unemployment Levels in North Sumatra Province. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 8(5), 589 - 596. https://doi.org/10.29207/resti.v8i5.5815
Section
Information Technology Articles