https://m2.mtmt.hu/ hun 2026-10-23 16:50 BookChapter 35749324 APPROVED true Sydney AI Centre, School of Computer Science, The University of Sydney, Australia JD Explore Academy Department of Automation, University of Science and Technology of China, China Conference code: 200372 Cited By :2 Export Date: 10 February 2025 Correspondence Address: Du, Y.; JD Explore Academyemail: duyuxuan123@gmail.com Correspondence Address: Liu, T.; Sydney AI Centre, Australia; email: tongliang.liu@sydney.edu.au Funding details: 2023-ZM01Z001 Funding details: National Natural Science Foundation of China, NSFC, 62276242 Funding details: National Natural Science Foundation of China, NSFC Funding details: 2022Z071078001 Funding details: Anhui Provincial Key Research and Development Plan, 202104a05020007 Funding details: Anhui Provincial Key Research and Development Plan Funding details: JL06521001Y, 20-163-14-LZ-001-004-01 Funding details: Australian Research Council, ARC, IC190100031, DP220102121, FT220100318, LP220200949, LP220100527 Funding details: Australian Research Council, ARC Funding details: CAAIXSJLJJ-2021-016B, CAAIXSJLJJ-2022-001A Funding text 1: Jun Yu was supported by the Natural Science Foundation of China (62276242), National Aviation Science Foundation (2022Z071078001), CAAI-Huawei MindSpore Open Fund (CAAIXSJLJJ-2021-016B, CAAIXSJLJJ-2022-001A), Anhui Province Key Research and Development Program (202104a05020007), Dreams Foundation of Jianghuai Advance Technology Center (2023-ZM01Z001), USTC-IAT Application Sci. & Tech. Achievement Cultivation Program (JL06521001Y), Sci. & Tech. Innovation Special Zone (20-163-14-LZ-001-004-01). Tongliang Liu is partially supported by the following Australian Research Council projects: FT220100318, DP220102121, LP220100527, LP220200949, IC190100031. The authors would give special thanks to Xinbiao Wang from Wuhan University for helpful discussions. 0 false 2025-02-28T12:52:45.268+0000 2025-02-28T09:37:54.569+0000 2025-02-10T11:17:14.431+0000 true 10084481 Toroczkai Annamária /api/admin/10084481 Admin Toroczkai Annamária (idézők, admin) true 5124601 2025-03-05T16:10:02.970+0000 2025-03-05T16:10:03.187+0000 Admin 10015168 /api/admin/10015168 Szakonyi Erzsebet (RAMKI admin 4) Szakonyi Erzsebet true 10015168 true SUSPECT 2025-02-28T09:37:54.844+0000 2025-03-05T16:10:03.187+0000 false false true 31 31 /api/publicationtype/31 PublicationType Egyéb konferenciaközlemény 4 true 31 BookChapter true 10000197 Conference paper true 31 31 /api/publicationtype/31 PublicationType Egyéb konferenciaközlemény 4 true 31 BookChapter /api/subtype/10000197 Konferenciaközlemény SubType Konferenciaközlemény (Egyéb konferenciaközlemény) 452 true 10000197 true 1 /api/category/1 Category Tudományos true 1 Lei, C. & & 2024 Quantum kernels hold great promise for offering computational advantages over classical learners, with the effectiveness of these kernels closely tied to the design of the quantum feature map. However, the challenge of designing effective quantum feature maps for real-world datasets, particularly in the absence of sufficient prior information, remains a significant obstacle. In this study, we present a data-driven approach that automates the design of problem-specific quantum feature maps. Our approach leverages feature-selection techniques to handle high-dimensional data on near-term quantum machines with limited qubits, and incorporates a deep neural predictor to efficiently evaluate the performance of various candidate quantum kernels. Through extensive numerical simulations on different datasets, we demonstrate the superiority of our proposal over prior methods, especially for the capability of eliminating the kernel concentration issue and identifying the feature map with prediction advantages. Our work not only unlocks the potential of quantum kernels for enhancing real-world tasks but also highlights the substantial role of deep learning in advancing quantum machine learning. © 2024 12th International Conference on Learning Representations, ICLR 2024. 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Goodfellow, Ian, Bengio, Yoshua, Courville, Aaron, (2016) Deep learning, , MIT press 1 false true Reference 63219079 /api/reference/63219079 2. Harrow, Aram W, Montanaro, Ashley, Quantum computational supremacy (2017) Nature, 549 (7671), pp. 203-209 2 false true Reference 63219047 /api/reference/63219047 3. Abbas, Amira, Sutter, David, Zoufal, Christa, Lucchi, Aurélien, Figalli, Alessio, Woerner, Stefan, The power of quantum neural networks (2021) Nature Computational Science, 1 (6), pp. 403-409 3 false true Reference 63219048 /api/reference/63219048 4. Abdi, Hervé, Williams, Lynne J, Principal component analysis (2010) Wiley interdisciplinary reviews: computational statistics, 2 (4), pp. 433-459 4 false true Reference 63219049 /api/reference/63219049 5. Akiba, Takuya, Sano, Shotaro, Yanase, Toshihiko, Ohta, Takeru, Koyama, Masanori, Optuna: A next-generation hyperparameter optimization framework (2019) Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 2623-2631 5 false true Reference 63219050 /api/reference/63219050 6. Altares-López, Sergio, Ribeiro, Angela, García-Ripoll, Juan José, Automatic design of quantum feature maps (2021) Quantum Science and Technology, 6 (4), p. 045015 6 false true Reference 63219051 /api/reference/63219051 7. Babbush, Ryan, McClean, Jarrod R, Newman, Michael, Gidney, Craig, Boixo, Sergio, Neven, Hartmut, Focus beyond quadratic speedups for error-corrected quantum advantage (2021) PRX Quantum, 2 (1), p. 010103 7 false true Reference 63219052 /api/reference/63219052 8. Bartlett, Marian Stewart, Littlewort, Gwen, Fasel, Ian, Movellan, Javier R, Real time face detection and facial expression recognition: Development and applications to human computer interaction (2003) 2003 Conference on computer vision and pattern recognition workshop, 5, pp. 53-53. , IEEE 8 false true Reference 63219053 /api/reference/63219053 9. Bergholm, Ville, Izaac, Josh, Schuld, Maria, Gogolin, Christian, Ahmed, Shahnawaz, Ajith, Vishnu, Sohaib Alam, M, Asadi, Ali, (2018) Pennylane: Automatic differentiation of hybrid quantum-classical computations, , arXiv preprint arXiv:1811.04968 9 false true Reference 63219054 /api/reference/63219054 10. Bharti, Kishor, Cervera-Lierta, Alba, Kyaw, Thi Ha, Haug, Tobias, Alperin-Lea, Sumner, Anand, Abhinav, Degroote, Matthias, Menke, Tim, Noisy intermediate-scale quantum algorithms (2022) Reviews of Modern Physics, 94 (1), p. 015004 10 false true Reference 63219055 /api/reference/63219055 11. Biamonte, Jacob, Wittek, Peter, Pancotti, Nicola, Rebentrost, Patrick, Wiebe, Nathan, Lloyd, Seth, Quantum machine learning (2017) Nature, 549 (7671), pp. 195-202 11 false true Reference 63219056 /api/reference/63219056 12. Blank, Carsten, Park, Daniel K, Rhee, June-Koo Kevin, Petruccione, Francesco, Quantum classifier with tailored quantum kernel (2020) npj Quantum Information, 6 (1), p. 41 12 false true Reference 63219057 /api/reference/63219057 13. Bradbury, James, Frostig, Roy, Hawkins, Peter, Johnson, Matthew James, Leary, Chris, Maclaurin, Dougal, Necula, George, Wanderman-Milne, Skye, (2018) Jax: Compos-able transformations of python+ numpy programs (v0. 2.5), , https://github.com/google/jax, Software 13 false true Reference 63219058 /api/reference/63219058 14. Buhmann, Martin Dietrich, Radial basis functions (2000) Acta numerica, 9, pp. 1-38 14 false true Reference 63219059 /api/reference/63219059 15. Camargo, Jorge E, González, Fabio A, A multi-class kernel alignment method for image collection summarization (2009) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009, pp. 545-552. , Guadalajara, Jalisco, Mexico, November 15-18, Proceedings 14, Springer, 2009 15 false true Reference 63219060 /api/reference/63219060 16. Canatar, Abdulkadir, Peters, Evan, Pehlevan, Cengiz, Wild, Stefan M, Shaydulin, Ruslan, Bandwidth enables generalization in quantum kernel models (2023) Transactions on Machine Learning Research 16 false true Reference 63219061 /api/reference/63219061 17. Caruana, Rich, Lawrence, Steve, Giles, C, Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping (2000) Advances in neural information processing systems, 13 17 false true Reference 63219062 /api/reference/63219062 18. Cerezo, Marco, Arrasmith, Andrew, Babbush, Ryan, Benjamin, Simon C, Endo, Suguru, Fujii, Keisuke, McClean, Jarrod R, Cincio, Lukasz, Variational quantum algorithms (2021) Nature Reviews Physics, 3 (9), pp. 625-644 18 false true Reference 63219063 /api/reference/63219063 19. Chapelle, Olivier, Haffner, Patrick, Vapnik, Vladimir N, Support vector machines for histogram-based image classification (1999) IEEE transactions on Neural Networks, 10 (5), pp. 1055-1064 19 false true Reference 63219064 /api/reference/63219064 20. Cristianini, N., Shawe-Taylor, J., Elisseeff, A., Kandola, J. S., On kernel-target alignment (2001) Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, , December 3-8, Vancouver, British Columbia, Canada], 2001 20 false true Reference 63219065 /api/reference/63219065 21. Dal Pozzolo, Andrea, Boracchi, Giacomo, Caelen, Olivier, Alippi, Cesare, Bontempi, Gianluca, Credit card fraud detection: a realistic modeling and a novel learning strategy (2017) IEEE transactions on neural networks and learning systems, 29 (8), pp. 3784-3797 21 false true Reference 63219066 /api/reference/63219066 22. Du, Yuxuan, Liu, Tongliang, Li, Yinan, Duan, Runyao, Tao, Dacheng, Quantum divide-and-conquer anchoring for separable non-negative matrix factorization (2018) Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 2093-2099 22 false true Reference 63219067 /api/reference/63219067 23. Du, Yuxuan, Hsieh, Min-Hsiu, Liu, Tongliang, You, Shan, Tao, Dacheng, Learnability of quantum neural networks (2021) PRX Quantum, 2 (4), p. 040337 23 false true Reference 63219068 /api/reference/63219068 24. Du, Yuxuan, Hsieh, Min-Hsiu, Liu, Tongliang, You, Shan, Tao, Dacheng, Quantum differentially private sparse regression learning (2022) IEEE Transactions on Information Theory, 68 (8), pp. 5217-5233 24 false true Reference 63219069 /api/reference/63219069 25. Du, Yuxuan, Huang, Tao, You, Shan, Hsieh, Min-Hsiu, Tao, Dacheng, Quantum circuit architecture search for variational quantum algorithms (2022) npj Quantum Information, 8 (1), p. 62 25 false true Reference 63219070 /api/reference/63219070 26. Du, Yuxuan, Yang, Yibo, Tao, Dacheng, Hsieh, Min-Hsiu, (2022) Demystify problem-dependent power of quantum neural networks on multi-class classification, , arXiv preprint arXiv:2301.01597, c 26 false true Reference 63219071 /api/reference/63219071 27. Duong, Trong, Truong, Sang T, Tam, Minh, Bach, Bao, Ryu, Ju-Young, Rhee, June-Koo Kevin, (2022) Quantum neural architecture search with quantum circuits metric and bayesian optimization, , arXiv preprint arXiv:2206.14115 27 false true Reference 63219072 /api/reference/63219072 28. Feynman, Richard P, Simulating physics with computers (2018) Feynman and computation, pp. 133-153. , CRC Press 28 false true Reference 63219073 /api/reference/63219073 29. Gao, Ya, Sun, Shiliang, An empirical evaluation of linear and nonlinear kernels for text classification using support vector machines (2010) 2010 seventh international conference on fuzzy systems and knowledge discovery, 4, pp. 1502-1505. , IEEE 29 false true Reference 63219074 /api/reference/63219074 30. Gentinetta, Gian, Sutter, David, Zoufal, Christa, Fuller, Bryce, Woerner, Stefan, (2023) Quantum kernel alignment with stochastic gradient descent, , arXiv preprint arXiv:2304.09899 30 false true Reference 63219075 /api/reference/63219075 31. Gilyén, András, Su, Yuan, Hao Low, Guang, Wiebe, Nathan, Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics (2019) Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, pp. 193-204 31 false true Reference 63219076 /api/reference/63219076 32. Glick, Jennifer, Covariant quantum kernels for data with group structure (2022) Bulletin of the American Physical Society, 67 32 false true Reference 63219078 /api/reference/63219078 33. Gouzien, Élie, Sangouard, Nicolas, Factoring 2048-bit rsa integers in 177 days with 13 436 qubits and a multimode memory (2021) Physical review letters, 127 (14), p. 140503 33 false true Reference 63219080 /api/reference/63219080 34. Harrow, Aram W, Hassidim, Avinatan, Lloyd, Seth, Quantum algorithm for linear systems of equations (2009) Physical review letters, 103 (15), p. 150502 34 false true Reference 63219081 /api/reference/63219081 35. Haug, Tobias, Self, Chris N, Kim, Myungshik, Quantum machine learning of large datasets using randomized measurements (2021) Machine Learning: Science and Technology 35 false true Reference 63219082 /api/reference/63219082 36. Havlíček, Vojtěch, Córcoles, Antonio D, Temme, Kristan, Harrow, Aram W, Kandala, Abhinav, Chow, Jerry M, Gambetta, Jay M, Supervised learning with quantum-enhanced feature spaces (2019) Nature, 567 (7747), pp. 209-212 36 false true Reference 63219083 /api/reference/63219083 37. He, Xiaofei, Niyogi, Partha, Locality preserving projections (2003) Advances in neural information processing systems, 16 37 false true Reference 63219084 /api/reference/63219084 38. He, Zhimin, Deng, Maijie, Zheng, Shenggen, Li, Lvzhou, Situ, Haozhen, (2023) Gsqas: Graph self-supervised quantum architecture search, , arXiv preprint arXiv:2303.12381, a 38 false true Reference 63219085 /api/reference/63219085 39. He, Zhimin, Zhang, Xuefen, Chen, Chuangtao, Huang, Zhiming, Zhou, Yan, Situ, Haozhen, A gnn-based predictor for quantum architecture search (2023) Quantum Information Processing, 22 (2), p. 128 39 false true Reference 63219086 /api/reference/63219086 40. Huang, Hsin-Yuan, Broughton, Michael, Mohseni, Masoud, Babbush, Ryan, Boixo, Sergio, Neven, Hartmut, McClean, Jarrod R, Power of data in quantum machine learning (2021) Nature communications, 12 (1), p. 2631 40 false true Reference 63219087 /api/reference/63219087 41. Huang, Yiming, Wang, Huiyuan, Du, Yuxuan, Yuan, Xiao, (2023) Coreset selection can accelerate quantum machine learning models with provable generalization, , arXiv preprint arXiv:2309.10441, a 41 false true Reference 63219088 /api/reference/63219088 42. Huang, Zhuo, Xia, Xiaobo, Shen, Li, Han, Bo, Gong, Mingming, Gong, Chen, Liu, Tongliang, Harnessing out-of-distribution examples via augmenting content and style (2023) ICLR 42 false true Reference 63219089 /api/reference/63219089 43. Hubregtsen, Thomas, Wierichs, David, Gil-Fuster, Elies, Derks, Peter-Jan HS, Faehrmann, Paul K, Meyer, Johannes Jakob, Training quantum embedding kernels on near-term quantum computers (2022) Physical Review A, 106 (4), p. 042431 43 false true Reference 63219090 /api/reference/63219090 44. Jäger, Jonas, Krems, Roman V, Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines (2023) Nature Communications, 14 (1), p. 576 44 false true Reference 63219091 /api/reference/63219091 45. Jaitly, Navdeep, Hinton, Geoffrey E, Vocal tract length perturbation (vtlp) improves speech recognition (2013) Proc. ICML Workshop on Deep Learning for Audio, Speech and Language, 117, p. 21 45 false true Reference 63219092 /api/reference/63219092 46. Jiang, Hao, Ching, Wai-Ki, Fai Cedric Yiu, Ka, Qiu, Yushan, Stationary mahalanobis kernel svm for credit risk evaluation (2018) Applied Soft Computing, 71, pp. 407-417 46 false true Reference 63219093 /api/reference/63219093 47. Kandala, Abhinav, Mezzacapo, Antonio, Temme, Kristan, Takita, Maika, Brink, Markus, Chow, Jerry M, Gambetta, Jay M, Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets (2017) nature, 549 (7671), pp. 242-246 47 false true Reference 63219094 /api/reference/63219094 48. Krunic, Zoran, Flöther, Frederik F, Seegan, George, Earnest-Noble, Nathan D, Shehab, Omar, Quantum kernels for real-world predictions based on electronic health records (2022) IEEE Transactions on Quantum Engineering, 3, pp. 1-11 48 false true Reference 63219095 /api/reference/63219095 49. Kübler, Jonas, Buchholz, Simon, Schölkopf, Bernhard, The inductive bias of quantum kernels (2021) Advances in Neural Information Processing Systems, 34, pp. 12661-12673 49 false true Reference 63219096 /api/reference/63219096 50. Lecun, Y., Bottou, L., Gradient-based learning applied to document recognition (1998) Proceedings of the IEEE, 86 (11), pp. 2278-2324 50 false true Reference 63219097 /api/reference/63219097 51. Lei, Cong, Zhu, Xiaofeng, Unsupervised feature selection via local structure learning and sparse learning (2018) Multimedia Tools and Applications, 77, pp. 29605-29622 51 false true Reference 63219098 /api/reference/63219098 52. Leone, Lorenzo, Oliviero, Salvatore FE, Cincio, Lukasz, Cerezo, M, (2022) On the practical usefulness of the hardware efficient ansatz, , arXiv preprint arXiv:2211.01477 52 false true Reference 63219099 /api/reference/63219099 53. Li, Yangding, Lei, Cong, Fang, Yue, Hu, Rongyao, Li, Yonggang, Zhang, Shichao, Unsupervised feature selection by combining subspace learning with feature self-representation (2018) Pattern Recognition Letters, 109, pp. 35-43 53 false true Reference 63219100 /api/reference/63219100 54. Linghu, Kehuan, Qian, Yang, Wang, Ruixia, Hu, Meng-Jun, Li, Zhiyuan, Li, Xuegang, Xu, Huikai, Zhao, Peng, (2022) Quantum circuit architecture search on a superconducting processor, , arXiv preprint arXiv:2201.00934 54 false true Reference 63219101 /api/reference/63219101 55. Liu, Minzhao, Liu, Junyu, Liu, Rui, Makhanov, Henry, Lykov, Danylo, Apte, Anuj, Alexeev, Yuri, Embedding learning in hybrid quantum-classical neural networks (2022) 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 79-86. , IEEE 55 false true Reference 63219102 /api/reference/63219102 56. Liu, Yunchao, Arunachalam, Srinivasan, Temme, Kristan, A rigorous and robust quantum speed-up in supervised machine learning (2021) Nature Physics, 17 (9), pp. 1013-1017 56 false true Reference 63219103 /api/reference/63219103 57. Lloyd, Seth, Mohseni, Masoud, Rebentrost, Patrick, Quantum principal component analysis (2014) Nature Physics, 10 (9), pp. 631-633 57 false true Reference 63219104 /api/reference/63219104 58. Lloyd, Seth, Schuld, Maria, Ijaz, Aroosa, Izaac, Josh, Killoran, Nathan, (2020) Quantum embeddings for machine learning, , arXiv preprint arXiv:2001.03622 58 false true Reference 63219105 /api/reference/63219105 59. Lu, Xudong, Pan, Kaisen, Yan, Ge, Shan, Jiaming, Wu, Wenjie, Yan, Junchi, Qas-bench: rethinking quantum architecture search and a benchmark (2023) International Conference on Machine Learning, pp. 22880-22898. , PMLR 59 false true Reference 63219106 /api/reference/63219106 60. Mandle, Anil Kumar, Jain, Pranita, Shrivastava, Shailendra Kumar, Protein structure prediction using support vector machine (2012) International Journal on Soft Computing, 3 (1), p. 67 60 false true Reference 63219107 /api/reference/63219107 61. Mitarai, Kosuke, Negoro, Makoto, Kitagawa, Masahiro, Fujii, Keisuke, Quantum circuit learning (2018) Physical Review A, 98 (3), p. 032309 61 false true Reference 63219108 /api/reference/63219108 62. Nakaji, Kouhei, Yamamoto, Naoki, Expressibility of the alternating layered ansatz for quantum computation (2021) Quantum, 5, p. 434 62 false true Reference 63219109 /api/reference/63219109 63. Ostaszewski, Mateusz, Trenkwalder, Lea M, Masarczyk, Wojciech, Scerri, Eleanor, Dunjko, Vedran, Reinforcement learning for optimization of variational quantum circuit architectures (2021) Advances in Neural Information Processing Systems, 34, pp. 18182-18194 63 false true Reference 63219110 /api/reference/63219110 64. Park, Jae-eun, Quanz, Brian, Wood, Stephen, Higgins, Heather, Harishankar, Ray, Practical application improvement to quantum svm: theory to practice (2020) Annual Conference on Neural Information Processing Systems 64 false true Reference 63219111 /api/reference/63219111 65. Paszke, Adam, Gross, Sam, Massa, Francisco, Lerer, Adam, Bradbury, James, Chanan, Gregory, Killeen, Trevor, Antiga, Luca, Pytorch: An imperative style, high-performance deep learning library (2019) Advances in neural information processing systems, 32 65 false true Reference 63219112 /api/reference/63219112 66. Pellow-Jarman, Rowan, Pillay, Anban, Sinayskiy, Ilya, Petruccione, Francesco, (2023) Hybrid genetic optimisation for quantum feature map design, , arXiv preprint arXiv:2302.02980 66 false true Reference 63219113 /api/reference/63219113 67. Peng, H., Long, F., Ding, C., Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy (2005) IEEE Transactions on Pattern Analysis & Machine Intelligence, 27 (8), pp. 1226-1238 67 false true Reference 63219114 /api/reference/63219114 68. Preskill, John, Quantum computing in the nisq era and beyond (2018) Quantum, 2, p. 79 68 false true Reference 63219115 /api/reference/63219115 69. Qian, Yang, Wang, Xinbiao, Du, Yuxuan, Wu, Xingyao, Tao, Dacheng, The dilemma of quantum neural networks (2022) IEEE Transactions on Neural Networks and Learning Systems 69 false true Reference 63219116 /api/reference/63219116 70. Rebentrost, Patrick, Mohseni, Masoud, Lloyd, Seth, Quantum support vector machine for big data classification (2014) Physical review letters, 113 (13), p. 130503 70 false true Reference 63219117 /api/reference/63219117 71. Schuld, Maria, Killoran, Nathan, Quantum machine learning in feature hilbert spaces (2019) Physical review letters, 122 (4), p. 040504 71 false true Reference 63219118 /api/reference/63219118 72. Shastry, Abhay, Patel, Apoorva, Bhattacharyya, Chiranjib, (2022) Reliable quantum kernel classification using fewer circuit evaluations, , arXiv preprint arXiv:2210.06971 72 false true Reference 63219119 /api/reference/63219119 73. Shaydulin, Ruslan, Wild, Stefan M, Importance of kernel bandwidth in quantum machine learning (2022) Physical Review A, 106 (4), p. 042407 73 false true Reference 63219120 /api/reference/63219120 74. Srivastava, Nitish, Hinton, Geoffrey, Krizhevsky, Alex, Sutskever, Ilya, Salakhutdinov, Ruslan, Dropout: a simple way to prevent neural networks from overfitting (2014) The journal of machine learning research, 15 (1), pp. 1929-1958 74 false true Reference 63219121 /api/reference/63219121 75. Sun, Shengyang, Zhang, Guodong, Wang, Chaoqi, Zeng, Wenyuan, Li, Jiaman, Grosse, Roger, Differentiable compositional kernel learning for gaussian processes (2018) International Conference on Machine Learning, pp. 4828-4837. , PMLR 75 false true Reference 63219122 /api/reference/63219122 76. Sünkel, Leo, Martyniuk, Darya, Mattern, Denny, Jung, Johannes, Paschke, Adrian, (2023) Ga4qco: Genetic algorithm for quantum circuit optimization, , arXiv preprint arXiv:2302.01303 76 false true Reference 63219123 /api/reference/63219123 77. Thanasilp, Supanut, Wang, Samson, Cerezo, Marco, Holmes, Zoe, Exponential concentration and untrainability in quantum kernel methods (2023) Bulletin of the American Physical Society 77 false true Reference 63219124 /api/reference/63219124 78. Thanasilp, Supanut, Wang, Samson, Nghiem, Nhat Anh, Coles, Patrick, Cerezo, Marco, Subtleties in the trainability of quantum machine learning models (2023) Quantum Machine Intelligence, 5 (1), p. 21 78 false true Reference 63219125 /api/reference/63219125 79. Tian, Jinkai, Sun, Xiaoyu, Du, Yuxuan, Zhao, Shanshan, Liu, Qing, Zhang, Kaining, Yi, Wei, Wu, Xingyao, Recent advances for quantum neural networks in generative learning (2023) IEEE Transactions on Pattern Analysis and Machine Intelligence 79 false true Reference 63219126 /api/reference/63219126 80. Torabian, Elham, Krems, Roman V, Compositional optimization of quantum circuits for quantum kernels of support vector machines (2023) Physical Review Research, 5 (1), p. 013211 80 false true Reference 63219127 /api/reference/63219127 81. Tsang, Ivor W, Kwok, James T, Cheung, Pak-Ming, Cristianini, Nello, Core vector machines: Fast svm training on very large data sets (2005) Journal of Machine Learning Research, 6 (4) 81 false true Reference 63219128 /api/reference/63219128 82. Wang, Xinbiao, Du, Yuxuan, Luo, Yong, Tao, Dacheng, Towards understanding the power of quantum kernels in the nisq era (2021) Quantum, 5, p. 531 82 false true Reference 63219129 /api/reference/63219129 83. Wu, Sau Lan, Sun, Shaojun, Guan, Wen, Zhou, Chen, Chan, Jay, Cheng, Chi Lung, Pham, Tuan, Zhang, Rui, Application of quantum machine learning using the quantum kernel algorithm on high energy physics analysis at the lhc (2021) Physical Review Research, 3 (3), p. 033221 83 false true Reference 63219130 /api/reference/63219130 84. Wu, Wenjie, Yan, Ge, Lu, Xudong, Pan, Kaisen, Yan, Junchi, Quantumdarts: differentiable quantum architecture search for variational quantum algorithms (2023) International Conference on Machine Learning, pp. 37745-37764. , PMLR, a 84 false true Reference 63219131 /api/reference/63219131 85. Wu, Yusen, Wu, Bujiao, Wang, Jingbo, Yuan, Xiao, Quantum phase recognition via quantum kernel methods (2023) Quantum, 7, p. 981 85 false true Reference 63219132 /api/reference/63219132 86. Yuan, Changan, Zhong, Zhi, Lei, Cong, Zhu, Xiaofeng, Hu, Rongyao, Adaptive reverse graph learning for robust subspace learning (2021) Information Processing & Management, 58 (6), p. 102733 86 false true Reference 63219133 /api/reference/63219133 87. Zhang, Anqi, Zhao, Shengmei, (2022) Evolutionary-based quantum architecture search, , arXiv preprint arXiv:2212.00421 87 false true Reference 63219134 /api/reference/63219134 88. Zhang, Shi-Xin, Hsieh, Chang-Yu, Zhang, Shengyu, Yao, Hong, Neural predictor based quantum architecture search (2021) Machine Learning: Science and Technology, 2 (4), p. 045027 88 false true Reference 63219135 /api/reference/63219135 89. Zhang, Shi-Xin, Hsieh, Chang-Yu, Zhang, Shengyu, Yao, Hong, Differentiable quantum architecture search (2022) Quantum Science and Technology, 7 (4), p. 045023 89 false true Reference 63219136 /api/reference/63219136 90. Zhu, Xiaofeng, Lei, Cong, Yu, Hao, Li, Yonggang, Gan, Jiangzhang, Zhang, Shichao, Robust graph dimensionality reduction (2018) IJCAI, pp. 3257-3263 90 false true Reference 63219137 /api/reference/63219137 91. Zhu, Xiaofeng, Hu, Rongyao, Lei, Cong, Thung, Kim Han, Zheng, Wei, Wang, Can, Low-rank hypergraph feature selection for multi-output regression (2019) World Wide Web, 22, pp. 517-531 91 false true /api/publication/35749324 Lei C. et al. NEURAL AUTO-DESIGNER FOR ENHANCED QUANTUM KERNELS. (2024) Megjelent: 12th International Conference on Learning Representations, ICLR 2024 p. &<div class="BookChapter Publication long-list"> <div class="authors"> <img title="Idézőközlemény" style="float: left" src="/frontend/resources/grid/publication-citation-icon.png"> <div class="autype autype0"> <span class="author-name" >Lei C. </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Du Y. ✉ </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Mi P. </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Yu J. </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Liu T. ✉ </span> </div> </div> <div class="title"><a href="/gui2/?mode=browse&params=publication;35749324" target="_blank">NEURAL AUTO-DESIGNER FOR ENHANCED QUANTUM KERNELS</a></div> <div class="InBook"><div class="chapter-in">In:</div> <div class="authors"> </div> <div class="booktitle"><a href="/gui2/?mode=browse&params=publication;35178194" target="_blank">12th International Conference on Learning Representations, ICLR 2024 </a></div> <div class="conference"> Konferencia helye, ideje: <span class="conference-date">2024.05.07. - 2024.05.11.</span> </div> : <span class="publishers">International Conference on Learning Representations, ICLR</span>, <span class="page"> p. &. </span> <span class="year">(2024)</span> </div> <div class="pub-footer"> <span class="language" xmlns="http://www.w3.org/1999/html">Nyelv: Angol | </span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <a style="color:black" title="85200596324" target="_blank" href="http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-85200596324"> Scopus </a> </span> </span> <span class="bookchapter-ids">Befoglaló link(ek):</span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="https://openreview.net/group?id=ICLR.cc/2024/Conference#tab-accept-oral" target="_blank" href="https://openreview.net/group?id=ICLR.cc/2024/Conference#tab-accept-oral"> Egyéb URL </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="https://iclr.cc/Conferences/2024" target="_blank" href="https://iclr.cc/Conferences/2024"> Egyéb URL </a> </span> </span> <div class="publication-citation"> <a target="_blank" href="/api/publication?cond=citations.related;eq;35749324&sort=publishedYear,desc&sort=title"> Idézett közlemények száma: 1 </a> </div> <div class="mtid"><span class="long-pub-mtid">Közlemény: 35749324</span> | <span class="status-data status-APPROVED"> Nyilvános </span> <span class="long-book-mtid">Befoglaló: 35178194</span> Idéző | <span class="type-subtype">Egyéb konferenciaközlemény ( Konferenciaközlemény ) </span> | <span class="pub-category">Tudományos</span> | <span class="publication-sourceOfData">Scopus</span> </div> <div class="lastModified">Utolsó módosítás: 2025.02.28. 10:37 Szakonyi Erzsebet (RAMKI admin 4) </div> <pre class="comment" style="margin-top: 0; margin-bottom: 0;"><u>Megjegyzés</u>: Sydney AI Centre, School of Computer Science, The University of Sydney, Australia JD Explore Academy Department of Automation, University of Science and Technology of China, China Conference code: 200372 Cited By :2 Export Date: 10 February 2025 Corresp...</pre> </div> </div>