Analysis and synthesis of feature map for kernel-based quantum classifier

• Hastie T, Tibshirani R, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd Edition. Springer series in statistics Springer

• Alpaydin E (2016) Machine learning: the new AI. MIT press essential knowledge. MIT press, cambridge MA

• Boser BE, Guyon IM (1992) Vapnik, VN. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, vol 5, pp 144–152

• Rebentrost P, Mohseni M, Lloyd S (2014) Quantum support vector machine for big data classification. Phys. Rev. Lett. 130503:113

  Google Scholar 

• Mitarai K, Negoro M, Kitagawa M, Fujii K (2018) Quantum circuit learning. Phys. Rev. A 032309:98

  Google Scholar 

• Farhi E, Neven H (2018) Classification with quantum neural networks on near term processors. arXiv:1802.06002

• Zhuang Q, Zhang Z (2019) Supervised learning enhanced by an entangled sensor network. arXiv:1901.09566

• Wilson CM, Otterbach JS, Tezak N, Smith RS, Crooks GE, da Silva MP (2018) Quantum kitchen sinks: an algorithm for machine learning on near-term quantum computers. arXiv:1806.08321

• Chatterjee R, Yu T (2017) Generalized coherent states, reproducing kernels, and quantum support vector machines. Quantum Inf Commun 17:1292

  MathSciNet  Google Scholar 

• Bishwas AK, Mani A, Palade V (2018) An all-pair quantum SVM approach for big data multiclass classification. Quantum Inf Process 17(10):282

  Article  MathSciNet  Google Scholar 

• Li T, Chakrabarti S, Wu X (2019) Sublinear quantum algorithms for training linear and kernel-based classifiers. In: Proceedings of the 36th International Conference on Machine Learning (ICML 2019), vol. PMLR 97, pp. 3815–3824

• Havlíček V, Córcoles AD, Temme K, Harrow AW, Kandala A, Chow JM, Gambetta JM (2019) Supervised learning with quantum-enhanced feature spaces. Nature 567(7747):209

  Article  Google Scholar 

• Schuld M, Killoran N (2019) Quantum machine learning in feature Hilbert spaces. Phys Rev Lett 040504:122

  Google Scholar 

• Bartkiewicz K, Gneiting C, Černoch A, Jiráková K, Lemr K, Nori F (2019) Experimental kernel-based quantum machine learning in finite feature space. arXiv:1906.04137v1

• Blank C, Park DK, Rhee JKK, Petruccione F (2019) Quantum classifier with tailored quantum kernel. arXiv:1909.02611

• Kusumoto T, Mitarai K, Fujii K, Kitagawa M, Negoro M (2019) Experimental quantum kernel machine learning with nuclear spins in a solid. arXiv:1911.12021

• Lloyd S, Schuld M, Ijaz A, Izaac J, Killoran N (2020) Quantum embeddings for machine learning. arXiv:2001.03622

• LaRose R, Coyle B (2020) Robust data encodings for quantum classifiers. arXiv:2003.01695

• Aronoff S (1985) The minimum accuracy value as an index of classification accuracy. Photogrammetric Engineering and Remote Sensing 51(1):99–111

  Google Scholar 

• Aleksandrowicz G, Alexander T, Barkoutsos P, Bello L, Ben-Haim Y, Bucher D, Cabrera-Hernández FJ, Carballo-Franquis J, Chen A, Chen CF, Chow JM, Córcoles-Gonzales AD, Cross AJ, Cross A, Cruz-Benito J, Culver C, González SDLP, Torre EDL, Ding D, Dumitrescu E, Duran I, Eendebak P, Everitt M, Sertage IF, Frisch A, Fuhrer A, Gambetta J, Gago BG, Gomez-Mosquera J, Greenberg D, Hamamura I, Havlicek V, Hellmers J, Herok Ł, Horii H, Hu S, Imamichi T, Itoko T, Javadi-Abhari A, Kanazawa N, Karazeev A, Krsulich K, Liu P, Luh Y, Maeng Y, Marques M, Martín-Fernández FJ, McClure DT, McKay D, Meesala S, Mezzacapo A, Moll N, Rodríguez DM, Nannicini G, Nation P, Ollitrault P, O’Riordan LJ, Paik H, Pérez J, Phan A, Pistoia M, Prutyanov V, Reuter M, Rice J, Davila AR, Rudy RHP, Ryu M, Sathaye N, Schnabel C, Schoute E, Setia K, Shi Y, Silva A, Siraichi Y, Sivarajah S, Smolin JA, Soeken M, Takahashi H, Tavernelli I, Taylor C, Taylour P, Trabing K, Treinish M, Turner W, Vogt-Lee D, Vuillot C, Wildstrom JA, Wilson J, Winston E, Wood C, Wood S, Wörner S, Akhalwaya IY, Zoufal C (2019) Qiskit: an open-source framework for quantum computing

• Bishop CM (2006) Pattern recognition and machine learning information science andstatistics. Springer-Verlag New York, Inc., Secaucus NJ

• Lanckriet GR, Cristianini N, Bartlett P, Ghaoui LE, Jordan MI (2004) Learning the kernel matrix with semidefinite programming. J Mach learn res 5:27–72

  MathSciNet  MATH  Google Scholar 

• Dioş L, Oltean M, Rogozan A, Pecuchet JP (2007) Improving SVM performance using a linear combination of kernels. In: International Conference on Adaptive and Natural Computing Algorithms, pp. 218–227. Springer, Berlin, Heidelberg

• Dietterich TG (2000) Ensemble methods in machine learning. In: International workshop on multiple classifier systems, pp. 1–15. Springer, Berlin, Heidelberg

• Schuld M, Petruccione F (2018) Quantum ensembles of quantum classifiers. Scientific reports 8(1):2772

  Article  Google Scholar 

• Wang X, Ma Y, Hsieh MH, Yung M (2019) Quantum speedup in adaptive boosting of binary classification. arXiv:1902.00869

• Ambainis A, Nayak A, Ta-shma A, Vazirani U (2002) Dense quantum coding and a lower bound for 1-way quantum automata. J. ACM 49:496–511

  Article  MathSciNet  Google Scholar 

• Iwama K, Nishimura H, Raymond R, Yamashita S (2007) Unbounded-error one-way classical and quantum communication complexity, Automata, languages and programming lecture notes in computer science, vol 4596. Springer, Berlin, Heidelberg

  Google Scholar 

Download references