Abstract
In this paper, we propose a novel hybrid quantum–classical convolutional neural network named QSurfNet, inspired by an efficient surface defect recognition model called SurfNetv2. SurfNetv2 is an established high-speed classical convolution neural network (CNN) model for image recognition, and QSurfNet further inherits the legacy by introducing quantum CNN (QCNN) layers, reducing the number of convolution blocks in the model architecture and the image size required for recognition. The QSurfNet architecture consists of a QCNN module, a feature extraction module, and a surface defect recognition module. The algorithm works on end-to-end supervised quantum machine learning and deep learning techniques to classify the surface defect categories of the surface defect image datasets. For this research, we used the 8 × 8-pixel and 12 × 12-pixel resolution RGB image information from the public Northeastern University dataset, and an industry-sourced calcium silicate board private dataset. We used principal component analysis for image dimensionality reduction across the R, G, and B channels, individually. We compare the performance of QSurfNet with six state-of-the-art methods on these datasets upon recognition results on test accuracy, recall, precision, and F1-Measure performance metrics. QSurfNet is novel in terms of the algorithm design methodology that can turn any classical CNN algorithm into state-of-the-art QCNN. Hence, the proposed methodology contributes to the practical feasibility of developing novel convolutional architecture designs of hybrid quantum–classical algorithms.
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du Castel, B.: Pattern activation/recognition theory of mind. Front. Comput. Neurosci. 9, 90 (2015)
Yann, L., Yoshua, B., Geoffrey, E.H.: Deep learning. Nature (2015). https://doi.org/10.1038/nature14539
Piron, C.: On the foundations of quantum physics. In: Quantum Mechanics, Determinism, Causality, and Particles, pp. 105–116. Springer (1976)
Maxwell, J.C.: VIII. A dynamical theory of the electromagnetic field. Philos. Trans. R. Soc. Lond. 155, 459–512 (1865). https://doi.org/10.1098/rstl.1865.0008
Nelkon, M.M., Parker, R.: Advanced Level Physics. Heinemann Educational Books Ltd (1970)
Benbarrad, T., Salhaoui, M., Kenitar, S.B., Arioua, M.: Intelligent machine vision model for defective product inspection based on machine learning. J. Sens. Actuator Netw. 10(1), 7 (2021)
De Stefano, V.: “Negotiating the algorithm”: automation, artificial intelligence, and labor protection. Comp. Labor Law Policy J. 41, 15 (2019). https://doi.org/10.2139/ssrn.3178233
Le, H.N., Bao, T.V., Debnath, N.C.: Computer vision–based system for automation and industrial applications. In: Artificial Intelligence and the Fourth Industrial Revolution, pp. 3–43. Jenny Stanford Publishing (2022)
Mishra, S., Tsai, C.-Y.: Design of superior parameterized quantum circuits for quantum image classification. Presented at the 2022 14th international conference on computer and automation engineering (ICCAE), Brisbane, Australia (2022)
Broughton, M., et al.: TensorFlow quantum: a software framework for quantum machine learning. arXiv preprint arXiv:2003.02989, pp. 56 (2020). https://doi.org/10.48550/arXiv.2003.02989
Tsai, C.-Y., Chen, H.-W.: SurfNetv2: an improved real-time SurfNet and its applications to defect recognition of calcium silicate boards. Sensors 20(16), 4356 (2020). https://doi.org/10.3390/s20164356
Arikan, S., Varanasi, K., Stricker, D.: Surface defect classification in real-time using convolutional neural networks. arXiv: Image and Video Processing 2019. https://doi.org/10.48550/arXiv.1904.04671
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016 (2016), pp. 770–778. https://doi.org/10.1109/CVPR.2016.90
Iandola, F., Moskewicz, M., Karayev, S., Girshick, R., Darrell, T., Keutzer, K.: DenseNet: implementing efficient convnet descriptor pyramids. arXiv : Computer Vision and Pattern Recognition 2014. https://doi.org/10.48550/arXiv.1404.1869
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR 2015), San Diego, CA (2015), Ed., 7–9 May 2015, p. 14. https://doi.org/10.48550/arXiv.1409.1556
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: "Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA (2018), pp. 4510–4520. https://doi.org/10.48550/arXiv.1801.04381
Brigham, E.O.: The Fast Fourier Transform and Its Applications. Prentice-Hall Inc, NJ, United States (1988)
Guifang, W., Haichao, Z., Xiuming, S., Jinwu, X., Ke, X.: A bran-new feature extraction method and its application to surface defect recognition of hot rolled strips (2007). https://doi.org/10.1109/ical.2007.4338916
Zhanjiang, Y., Xiaozhou, L., Huadong, Y., Dan, X., Aimei, L., Hao, L.: Research on surface defect inspection for small magnetic rings (2009). https://doi.org/10.1109/icma.2009.5246333
Wang, F.-L., Zuo, B.: Detection of surface cutting defect on magnet using Fourier image reconstruction. J. Cent. South Univ. 23(5), 1123–1131 (2016). https://doi.org/10.1007/s11771-016-0362-y
Chan, C.-H., Pang, G.K.: Fabric defect detection by Fourier analysis. IEEE Trans. Ind. Appl. 36(5), 1267–1276 (2000). https://doi.org/10.1109/28.871274
Hu, G.-H., Wang, Q.-H., Zhang, G.-H.: Unsupervised defect detection in textiles based on Fourier analysis and wavelet shrinkage. Appl. Opt. 54(10), 2963–2980 (2015). https://doi.org/10.1364/AO.54.002963
Mahajan, P., Kolhe, S., Patil, P.: A review of automatic fabric defect detection techniques. Adv. Comput. Res. 1(2), 18–29 (2009)
Boujelbene, R., Jemaa, Y.B., Zribi, M.: A comparative study of recent improvements in wavelet-based image coding schemes. Multimed. Tools Appl. 78(2), 1649–1683 (2019). https://doi.org/10.1007/s11042-018-6262-4
Shensa, M.J.: The discrete wavelet transform: wedding the a trous and Mallat algorithms. IEEE Trans. Signal Process. 40(10), 2464–2482 (1992). https://doi.org/10.1109/78.157290
Abbate, A., Koay, J., Frankel, J., Schroeder, S.C., Das, P.: Signal detection and noise suppression using a wavelet transform signal processor: application to ultrasonic flaw detection. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 44(1), 14–26 (1997). https://doi.org/10.1109/58.585186
Rosenboom, L., Kreis, T., Jüptner, W.: Surface description and defect detection by wavelet analysis. Meas. Sci. Technol. 22(4), 045102 (2011). https://doi.org/10.1088/0957-0233/22/4/045102
Wu, X.-Y., Xu, K., Xu, J.-W.: Application of undecimated wavelet transform to surface defect detection of hot rolled steel plates. In: 2008 Congress on Image and Signal Processing, vol. 4, pp. 528–532. IEEE (2008). https://doi.org/10.1109/CISP.2008.278
Jeon, Y.-J., Yun, J.P., Choi, D.-C., Kim, S.W.: Defect detection algorithm for corner cracks in steel billet using discrete wavelet transform. In: 2009 ICCAS-SICE, Fukuoka, Japan, 18–21 Aug 2009, pp. 2769–2773. IEEE (2009)
Chang, Q., Zhang, Y., Sun, Z.: Research on surface defect detection algorithm of ice-cream bars based on clustering. In: 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 537–541. IEEE (2019)
Khumaidi, A., Yuniarno, E.M., Purnomo, M.H.: Welding defect classification based on convolution neural network (CNN) and Gaussian kernel. In: 2017 International Seminar on Intelligent Technology and Its Applications (ISITIA), pp. 261–265. IEEE (2017). https://doi.org/10.1109/ISITIA.2017.8124091
Maestro-Watson, D., Balzategui, J., Eciolaza, L., Arana-Arexolaleiba, N.: Deep learning for deflectometric inspection of specular surfaces. In: The 13th International Conference on Soft Computing Models in Industrial and Environmental Applications, pp. 280-289. Springer (2018)
Song, L., Li, X., Yang, Y., Zhu, X., Guo, Q., Yang, H.: Detection of micro-defects on metal screw surfaces based on deep convolutional neural networks. Sensors 18(11), 3709 (2018). https://doi.org/10.3390/s18113709
Zhang, A., et al.: Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network. Comput. Aided Civ. Infrastruct. Eng. 32(10), 805–819 (2017). https://doi.org/10.1111/mice.12297
Wei, R., Bi, Y.: Research on recognition technology of aluminum profile surface defects based on deep learning. Materials 12(10), 1681 (2019). https://doi.org/10.3390/ma12101681
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Sison, H., Konghuayrob, P., Kaitwanidvilai, S.: A convolutional neural network for segmentation of background texture and defect on copper clad lamination surface. In: 2018 International Conference on Engineering, Applied Sciences, and Technology (ICEAST), pp. 1–4. IEEE (2018)
Cuevas, E., Zaldivar, D., Pérez-Cisneros, M.: A novel multi-threshold segmentation approach based on differential evolution optimization. Expert Syst. Appl. 37(7), 5265–5271 (2010). https://doi.org/10.1016/j.eswa.2010.01.013
Tang, J.: A color image segmentation algorithm based on region growing. In: 2010 2nd International Conference on Computer Engineering and Technology, vol. 6, pp. V6–634-V6–637. IEEE (2010). https://doi.org/10.1109/ICCET.2010.5486012
Chuang, K.-S., Tzeng, H.-L., Chen, S., Wu, J., Chen, T.-J.: Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imaging Graph. 30(1), 9–15 (2006). https://doi.org/10.1016/j.compmedimag.2005.10.001
Dhanachandra, N., Manglem, K., Chanu, Y.J.: Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Comput. Sci. 54, 764–771 (2015)
Mia, S., Rahman, M.M.: An efficient image segmentation method based on linear discriminant analysis and K-means algorithm with automatically splitting and merging clusters. Int. J. Imaging Robot. 18(1), 62–72 (2018)
Xu, Y., Li, D., Xie, Q., Wu, Q., Wang, J.: Automatic defect detection and segmentation of tunnel surface using modified Mask R-CNN. Measurement 178, 109316 (2021). https://doi.org/10.1016/j.measurement.2021.109316
Xu, R., Hao, R., Huang, B.: Efficient surface defect detection using self-supervised learning strategy and segmentation network. Adv. Eng. Inform. 52, 101566 (2022). https://doi.org/10.1016/j.aei.2022.101566
Funck, J., Zhong, Y., Butler, D., Brunner, C., Forrer, J.: Image segmentation algorithms applied to wood defect detection. Comput. Electron. Agric. 41(1–3), 157–179 (2003). https://doi.org/10.1016/S0168-1699(03)00049-8
Celik, T., Tjahjadi, T.: Unsupervised colour image segmentation using dual-tree complex wavelet transform. Comput. Vis. Image Underst. 114(7), 813–826 (2010). https://doi.org/10.1016/j.cviu.2010.03.002
Lo, E.H.S., Pickering, M.R., Frater, M.R., Arnold, J.F.: Image segmentation from scale and rotation invariant texture features from the double dyadic dual-tree complex wavelet transform. Image Vis. Comput. 29(1), 15–28 (2011). https://doi.org/10.1016/j.imavis.2010.08.004
Bergholm, V., et al.: Pennylane: automatic differentiation of hybrid quantum-classical computations. arXiv preprint arXiv:1811.04968 (2018)
Gonzalez, C.: Cloud based QC with Amazon braket. Digit. Welt 5(2), 14–17 (2021). https://doi.org/10.1007/s42354-021-0330-z
Guerreschi, G.G., Hogaboam, J., Baruffa, F., Sawaya, N.P.: Intel quantum simulator: a cloud-ready high-performance simulator of quantum circuits. Quantum Sci. Technol. 5(3), 034007 (2020)
Das, M., Miguel, A.D., Mehrotra, A., Sahgal, V.: Quantum defect analyser. In: BMW Group Quantumm Computing Challenge (2021). [Online]. Available: https://github.com/iotaisolutions/BMWQuantumChallenge2021/blob/main/Reports/BMW_Quantum_Computing_Challenge_Solution%20Description.pdf
Schuetz, M., Shishenina, E., Klepsch, J., Luckow, A.: Use case insights from the BMW Group quantum challenge. In: AWS Invent, Las Vegas, Nevada (2021), p. 23. [Online]. Available: https://d1.awsstatic.com/events/reinvent/2021/Use_case_insights_from_the_BMW_Group_quantum_challenge_QTC304.pdf
Glick, J.R., et al.: Covariant quantum kernels for data with group structure. arXiv : Quantum Physics, p. 18 (2021). https://doi.org/10.48550/arXiv.2105.03406
John, P.: Quantum computing in the NISQ era and beyond. Quantum 2, 79 (2018). https://doi.org/10.22331/q-2018-08-06-79
Scott, A.: Read the fine print. Nat. Phys. (2015). https://doi.org/10.1038/nphys3272
Ian, G., Yoshua, B., Aaron, C.: Deep learning (2016). [Online]. Available: https://www.deeplearningbook.org/
Fei, Y., Salvador, E.V.-A.: Quantum image processing. Int. J. Quantum Inf. (2020). https://doi.org/10.1007/978-981-32-9331-1
Mateusz, O., Przemysław, S., Piotr, G.: Quantum image classification using principal component analysis. Theor. Appl. Inform. 27(1), 1–12 (2015). https://doi.org/10.20904/271001
Jolliffe, I.: Principal component analysis. Springer Series in Statistics, pp. 338–372 (1986). https://doi.org/10.1002/9781118445112.stat06472
Liberty, E., Woolfe, F., Martinsson, P.-G., Rokhlin, V., Tygert, M.: Randomized algorithms for the low-rank approximation of matrices. Proc. Natl. Acad. Sci. 104(51), 20167–20172 (2007). https://doi.org/10.1073/pnas.0709640104
Seth, L., Seth, L., Masoud, M., Patrick, R.: Quantum principal component analysis. Nat. Phys. 10(9), 631–633 (2014). https://doi.org/10.1038/nphys3029
Ha, J., Heo, J.: Performance comparison of quantum phase estimation algorithm with different number of register qubits on noisy quantum processor. Presented at the IEEE Region 10 Symposium (TENSYMP), Jeju, Republic of Korea, 23–25 Aug 2021 (2021)
Zhaokai, L., et al.: Resonant quantum principal component analysis. Sci. Adv. 7(34), 25–29 (2021). https://doi.org/10.1126/sciadv.abg2589
Rebentrost, P., Mohseni, M., Lloyd, S.: Quantum support vector machine for big data classification. Phys. Rev. Lett. 113(13), 130503 (2014). https://doi.org/10.1103/PhysRevLett.113.130503
Delilbasic, A., Cavallaro, G., Willsch, M., Melgani, F., Riedel, M., Michielsen, K.: Quantum support vector machine algorithms for remote sensing data classification. Presented at the IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 11–16 July 2021, pp. 2153-7003 (2021)
Alina, O.S., Shapovalova, N.: Classification problem solving using quantum machine learning mechanisms. In: 4th Workshop for Young Scientists in Computer Science & Software Engineering, Kryvyi Rih, Ukraine, vol. 6, no. 7, CEUR Workshop Proceedings, p. 8 (2022). [Online]. Available: https://ceur-ws.org/Vol-3077/paper06.pdf. [Online]. Available: https://ceur-ws.org/Vol-3077/paper06.pdf
Kariya, A., Behera, B.K.: Investigation of Quantum Support Vector Machine for Classification in NISQ era. arXiv: Quantum Physics (2021). https://doi.org/10.48550/arXiv.2112.06912
Rana, A., Vaidya, P., Gupta, G.: A comparative study of quantum support vector machine algorithm for handwritten recognition with support vector machine algorithm. Mater. Today Proc. 56, 2025–2030 (2022). https://doi.org/10.1016/j.matpr.2021.11.350
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967). https://doi.org/10.1109/TIT.1967.1053964
Lloyd, S., Mohseni, M., Rebentrost, P.: Quantum algorithms for supervised and unsupervised machine learning. arXiv preprint arXiv:1307.0411 (2013)
Li, J., Lin, S., Yu, K., Guo, G.: Quantum K-nearest neighbor classification algorithm based on Hamming distance. Quantum Inf. Process. 21(1), 1–17 (2022). https://doi.org/10.1007/s11128-021-03361-0
Zak, M., Williams, C.P.: Quantum neural nets. Int. J. Theor. Phys. 37(2), 651–684 (1998). https://doi.org/10.1023/A:1026656110699
Schuld, M., Sinayskiy, I., Petruccione, F.: An introduction to quantum machine learning. Contemp. Phys. 56(2), 172–185 (2015). https://doi.org/10.1080/00107514.2014.964942
McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biol. 5(4), 115–133 (1943)
Maria, S., Sinayskiy, I., Ilya, S., Francesco, P.: The quest for a quantum neural network. Quantum Inf. Process. 13(11), 2567–2586 (2014)
Schuld, M., Bocharov, A., Svore, K.M., Wiebe, N.: Circuit-centric quantum classifiers. Phys. Rev. A 101(3), 032308 (2020)
Schuld, M., Petruccione, F.: Supervised Learning with Quantum Computers, p. 287. Springer, Berlin (2018)
Mari, A., Bromley, T., Izaac, J., Schuld, M., Killoran, N.: Transfer learning in hybrid classical-quantum neural networks. Quantum (2020). https://doi.org/10.22331/q-2020-10-09-340
Gawron, P., Lewiński, S.: Multi-spectral image classification with quantum neural network. In: IGARSS 2020–2020 IEEE International Geoscience and Remote Sensing Symposium, 26 Sep -2 Oct 2020, pp. 3513–3516 (2020). https://doi.org/10.1109/IGARSS39084.2020.9323065
Natansh, M., et al.: Medical image classification via quantum neural networks. arXiv : Quantum Physics (2021). https://doi.org/10.48550/arXiv.2109.01831
Alam, M., Satwik, K., Topaloglu, R., Swaroop, G.: ICCAD special session paper: quantum-classical hybrid machine learning for image classification. Presented at the 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), Munich, Germany, 01–04 Nov 2021 (2021)
Kaya, M., Hajimirza, S.: Using a novel transfer learning method for designing thin film solar cells with enhanced quantum efficiencies. Sci. Rep. 9(1), 1–10 (2019). https://doi.org/10.1038/s41598-019-41316-9
Azevedo, V., Silva, C., Dutra, I.: Quantum transfer learning for breast cancer detection. Quantum Mach. Intell. 4(5), 1–14 (2022). https://doi.org/10.1007/s42484-022-00062-4
Mittal, S., Dana, S.K.: Gender recognition from facial images using hybrid classical-quantum neural network. In: 2020 IEEE Students Conference on Engineering & Systems (SCES), pp. 1–6. IEEE (2020) https://doi.org/10.1109/SCES50439.2020.9236711
Zhou, J., Gan, Q., Krzyżak, A., Suen, C.Y.: Recognition of handwritten numerals by quantum neural network with fuzzy features. Int. J. Doc. Anal. Recognit. (IJDAR) 2(1), 30–36 (1999). https://doi.org/10.1007/s100320050034
Mittal, H., Saraswat, M., Bansal, J.C., Nagar, A.: Fake-face image classification using improved quantum-inspired evolutionary-based feature selection method. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 989–995. IEEE (2020). https://doi.org/10.1109/SSCI47803.2020.9308337
Xu, Y., Zhang, X., Gai, H.: Quantum neural networks for face recognition classifier. Procedia Eng. 15, 1319–1323 (2011)
Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nat. Phys. 15(2), 1273–1278 (2019)
Guoming, C., et al.: Quantum convolutional neural network for image classification. Presented at the 2020 8th International Conference on Digital Home (ICDH), Dalian, China (2020)
Seunghyeok, O., Jaeho, C., Jong-Kook, K., Joongheon, K.: Quantum convolutional neural network for resource-efficient image classification: a quantum random access memory (QRAM) approach. In: 2021 International Conference on Information Networking (ICOIN) (2021). https://doi.org/10.1109/icoin50884.2021.9333906
Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Hybrid Quantum-Classical Graph Convolutional Network. arXiv: Machine Learning, no. arXiv:2101.06189 (2021). https://doi.org/10.48550/arXiv.2101.06189
Chen, S.Y.-C., Wei, T.-C., Zhang, C., Yu, H., Yoo, S.: Quantum convolutional neural networks for high energy physics data analysis. Phys. Rev. Res. 4(1), 013231 (2022). https://doi.org/10.1103/PhysRevResearch.4.013231
Shijie, W., YanHu, C., ZengRong, Z., Gui Lu, L.: A quantum convolutional neural network on NISQ devices. AAPPS Bull. 32(1), 1–11 (2022). https://doi.org/10.1007/s43673-021-00030-3
Hur, T., Kim, L., Park, D.K.: Quantum convolutional neural network for classical data classification. Quantum Mach. Intell. 4(3), 18 (2022). https://doi.org/10.1007/s42484-021-00061-x
Chao-Han Huck, Y., et al.: Decentralizing feature extraction with quantum convolutional neural network for automatic speech recognition. Presented at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6523–6527 (2021)
Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information, 10th Anniversary Edition. Cambridge University Press (2010). https://doi.org/10.1017/CBO9780511976667
Willsch, M., Willsch, D., Michielsen, K.: Lecture Notes: Programming Quantum Computers. arXiv :Quantum Physics (2022). https://doi.org/10.48550/arXiv.2201.02051
Schuld, M., Sinayskiy, I., Petruccione, F.: The quest for a quantum neural network. Quantum Inf. Process. 13(11), 2567–2586 (2014). https://doi.org/10.1007/s11128-014-0809-8
James, S., Josh, I., Nathan, K., Giuseppe, C.: Quantum natural gradient. Quantum 4(269), 15 (2020). https://doi.org/10.22331/q-2020-05-25-269
Ville, B., et al.: PennyLane: automatic differentiation of hybrid quantum-classical computations. arXiv: Quantum Physics (2018). https://doi.org/10.48550/arXiv.1811.04968
Bartkiewicz, K., Gneiting, C., Černoch, A., Jiráková, K., Lemr, K., Nori, F.: Experimental kernel-based quantum machine learning in finite feature space. Sci. Rep. 10(1), 12356 (2020). https://doi.org/10.1038/s41598-020-68911-5
Sainadh, U.S.: An efficient quantum algorithm and circuit to generate eigenstates of SU (2) and SU (3) representations. arXiv: Quantum Physics 1309.2736, pp. 121 (2013). https://doi.org/10.48550/arXiv.1309.2736
Iordanis, K., Jonas, L., Anupam, P.: Quantum algorithms for deep convolutional neural networks. In: Eighth International Conference on Learning Representations. (2020). https://doi.org/10.48550/arXiv.1911.01117.
Egger, D.J., et al.: Quantum computing for finance: State-of-the-art and future prospects. IEEE Trans. Quantum Eng. 1, 1–24 (2020). https://doi.org/10.1109/TQE.2020.3030314
Ryan, S., et al.: Stochastic gradient descent for hybrid quantum-classical optimization. Quantum (2020). https://doi.org/10.22331/q-2020-08-31-314
Szegedy, C., Toshev, A., Erhan, D.: Deep neural networks for object detection. Presented at the Proceedings of the 26th International Conference on Neural Information Processing Systems, vol. 2, Lake Tahoe, Nevada (2013)
Vedran, D., Jacob, M.T., Hans, J.B.: Quantum-enhanced machine learning. Phys. Rev. Lett. 117(13), 130501 (2016). https://doi.org/10.1103/PhysRevLett.117.130501
Hochreiter, S.: The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 6(02), 107–116 (1998). https://doi.org/10.1142/S0218488598000094
Arthur, P., Marco, C., Samson, W., Tyler, V., Andrew, T.S., Patrick, J.C.: Absence of barren plateaus in quantum convolutional neural networks. Phys. Rev. X (2021). https://doi.org/10.1103/physrevx.11.041011
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This research was supported by the Ministry of Science and Technology of Taiwan, R.O.C., under grant MOST 111-2221-E-032-031.
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Mishra, S., Tsai, CY. QSurfNet: a hybrid quantum convolutional neural network for surface defect recognition. Quantum Inf Process 22, 179 (2023). https://doi.org/10.1007/s11128-023-03930-5
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DOI: https://doi.org/10.1007/s11128-023-03930-5