Fetching the paper…
Reading the bibliography…
Exploiting the properties of quantum information to the benefit of machine learning models is perhaps the most active field of research in quantum computation.
Aronszajn, N.: Theory of reproducing kernels. Transactions of the American mathematical society 68
1950
Earlier work this paper cites.
MacQueen, J.: Classification and analysis of multivariate observations. In: 5th Berkeley Symp. Math. Statist. Probability, pp. 281–297 (1967)
1967
Earlier work this paper cites.
Lipow, M.: Number of faults per line of code. IEEE Transactions on software Engineering (4), 437–439 (1982)
1982
Earlier work this paper cites.
Kirkpatrick, S., Gelatt Jr, C.D., Vecchi, M.P.: Optimization by simulated annealing. science 220
1983
Earlier work this paper cites.
Feynman, R.P.: Quantum mechanical computers. Optics news 11
1985
Earlier work this paper cites.
Deutsch, D.: Quantum theory, the church–turing principle and the universal quantum computer. Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences 400
1985
Earlier work this paper cites.
Cortes, C., Vapnik, V.: Support-vector networks. Machine learning 20
1995
Earlier work this paper cites.
Forrest, S.: Genetic algorithms. ACM Computing Surveys (CSUR) 28
1996
Earlier work this paper cites.
Schölkopf, B., Smola, A., Müller, K.-R.: Kernel principal component analysis. In: International Conference on Artificial Neural Networks, pp. 583–588 (1997). Springer
1997
Earlier work this paper cites.
Ball, K., et al
1997
Earlier work this paper cites.
Farhi, E., Goldstone, J., Gutmann, S., Sipser, M.: Quantum computation by adiabatic evolution. arXiv preprint quant-ph/0001106 (2000)
2000
Earlier work this paper cites.
Schölkopf, B., Herbrich, R., Smola, A.J.: A generalized representer theorem. In: International Conference on Computational Learning Theory, pp. 416–426 (2001). Springer
2001
Earlier work this paper cites.
Yang, M.-H.: Face recognition using kernel methods. Advances in neural information processing systems 14
2001
Earlier work this paper cites.
Cristianini, N., Shawe-Taylor, J., Elisseeff, A., Kandola, J.: On kernel-target alignment. Advances in neural information processing systems 14
2001
Earlier work this paper cites.
Raussendorf, R., Briegel, H.J.: A one-way quantum computer. Physical review letters 86
2001
Earlier work this paper cites.
Kitaev, A.Y.: Fault-tolerant quantum computation by anyons. Annals of Physics 303
2003
Earlier work this paper cites.
Pérez-Cruz, F., Bousquet, O.: Kernel methods and their potential use in signal processing. IEEE Signal Processing Magazine 21
2004
Earlier work this paper cites.
Bach, F.R., Lanckriet, G.R., Jordan, M.I.: Multiple kernel learning, conic duality, and the smo algorithm. In: Proceedings of the Twenty-first International Conference on Machine Learning, p. 6 (2004)
2004
Earlier work this paper cites.
Van Tonder, A.: A lambda calculus for quantum computation. SIAM Journal on Computing 33
2004
Earlier work this paper cites.
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). MIT Press, Cambridge, MA, USA (2005)
2005
Earlier work this paper cites.
Ömer, B.: Classical concepts in quantum programming. International Journal of Theoretical Physics 44
2005
Earlier work this paper cites.
Camps-Valls, G.: Kernel Methods in Bioengineering, Signal and Image Processing. Igi Global, ??? (2006)
2006
Earlier work this paper cites.
Ben-Hur, A., Ong, C.S., Sonnenburg, S., Schölkopf, B., Rätsch, G.: Support vector machines and kernels for computational biology. PLoS computational biology 4
2008
Earlier work this paper cites.
Coecke, B., Duncan, R.: Interacting quantum observables: categorical algebra and diagrammatics. New Journal of Physics 13
2011
Earlier work this paper cites.
Murphy, K.P.: Machine Learning: a Probabilistic Perspective. MIT press, Cambridge, MA, USA (2012)
2012
Earlier work this paper cites.
Green, A.S., Lumsdaine, P.L., Ross, N.J., Selinger, P., Valiron, B.: Quipper: a scalable quantum programming language. In: Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 333–342 (2013)
2013
Earlier work this paper cites.
Wang, G., Qi, J.: Pet image reconstruction using kernel method. IEEE transactions on medical imaging 34
2014
Earlier work this paper cites.
Duvenaud, D.: Automatic model construction with gaussian processes. PhD thesis, University of Cambridge (2014)
2014
Earlier work this paper cites.
Gidney, C.: Quirk. GitHub. Available at: https://github.com/Strilanc/Quirk (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Chitambar, E., Leung, D., Mančinska, L., Ozols, M., Winter, A.: Everything you always wanted to know about locc (but were afraid to ask). Communications in Mathematical Physics 328
2014
Earlier work this paper cites.
Chollet, F., et al.: Keras. GitHub (2015). https://github.com/fchollet/keras
2015
Earlier work this paper cites.
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org (2015). https://www.tensorflow.org/
2015
Cited alongside, same era.
Montanaro, A.: Quantum algorithms: an overview. npj Quantum Information 2
2016
Cited alongside, same era.
Fidler, F., Chee, Y.E., Wintle, B.C., Burgman, M.A., McCarthy, M.A., Gordon, A.: Metaresearch for evaluating reproducibility in ecology and evolution. BioScience 67
2017
Cited alongside, same era.
Kübler, J., Buchholz, S., Schölkopf, B.: The inductive bias of quantum kernels. Advances in Neural Information Processing Systems 34
2021
Later among the works it cites.
Huang, H.-Y., Broughton, M., Mohseni, M., Babbush, R., Boixo, S., Neven, H., McClean, J.R.: Power of data in quantum machine learning. Nature Communications 12
2021
Later among the works it cites.
Liu, Y., Arunachalam, S., Temme, K.: A rigorous and robust quantum speed-up in supervised machine learning. Nature Physics 17
2021
Later among the works it cites.
Di Pierro, A., Incudini, M.: Quantum machine learning and fraud detection. In: Protocols, Strands, and Logic, pp. 139–155. Springer, Cham, Germany (2021)
2021
Later among the works it cites.
Peters, E., Caldeira, J., Ho, A., Leichenauer, S., Mohseni, M., Neven, H., Spentzouris, P., Strain, D., Perdue, G.N.: Machine learning of high dimensional data on a noisy quantum processor. npj Quantum Information 7
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
Preskill, J.: Quantum computing in the nisq era and beyond. Quantum 2
2018
Cited alongside, same era.
Mitarai, K., Negoro, M., Kitagawa, M., Fujii, K.: Quantum circuit learning. Physical Review A 98
2018
Cited alongside, same era.
McClean, J.R., Boixo, S., Smelyanskiy, V.N., Babbush, R., Neven, H.: Barren plateaus in quantum neural network training landscapes. Nature communications 9
2018
Cited alongside, same era.
Rojo-Álvarez, J.L., Martínez-Ramón, M., Munoz-Mari, J., Camps-Valls, G.: Digital Signal Processing with Kernel Methods. John Wiley & Sons, New York, NY, USA (2018)
2018
Cited alongside, same era.
Liu, N., Rebentrost, P.: Quantum machine learning for quantum anomaly detection. Phys. Rev. A 97
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Steiger, D.S., Häner, T., Troyer, M.: Projectq: an open source software framework for quantum computing. Quantum 2
2018
Cited alongside, same era.
Schuld, M., Killoran, N.: Quantum machine learning in feature hilbert spaces. Physical Review Letters 122
2019
Cited alongside, same era.
2021
Later among the works it cites.
Wang, X., Du, Y., Luo, Y., Tao, D.: Towards understanding the power of quantum kernels in the nisq era. Quantum 5
2021
Later among the works it cites.
Kusumoto, T., Mitarai, K., Fujii, K., Kitagawa, M., Negoro, M.: Experimental quantum kernel trick with nuclear spins in a solid. npj Quantum Information 7
2021
Later among the works it cites.
Anis, M.S., Abby-Mitchell, Abraham, H., et al.: Qiskit: An Open-source Framework for Quantum Computing (2021). https://doi.org/10.5281/zenodo.2573505
2021
Later among the works it cites.
Campos, J., Souto, A.: Qbugs: A collection of reproducible bugs in quantum algorithms and a supporting infrastructure to enable controlled quantum software testing and debugging experiments. In: 2021 IEEE/ACM 2nd International Workshop on Quantum Software Engineering (Q-SE), pp. 28–32 (2021). IEEE
2021
Later among the works it cites.
Mineault, P., Nozawa, K.: patrickmineault/codebook: 1.0.0. Zenodo (2021). https://doi.org/10.5281/zenodo.5796873 . https://doi.org/10.5281/zenodo.5796873
2021
Later among the works it cites.
2021
Later among the works it cites.
Altares-López, S., Ribeiro, A., García-Ripoll, J.J.: Automatic design of quantum feature maps. Quantum Science and Technology 6
2021
Later among the works it cites.
Efthymiou, S., Ramos-Calderer, S., Bravo-Prieto, C., Pérez-Salinas, A., García-Martín, D., Garcia-Saez, A., Latorre, J.I., Carrazza, S.: Qibo: a framework for quantum simulation with hardware acceleration. Quantum Science and Technology 7
2021
Later among the works it cites.
Madsen, L.S., Laudenbach, F., Askarani, M.F., Rortais, F., Vincent, T., Bulmer, J.F., Miatto, F.M., Neuhaus, L., Helt, L.G., Collins, M.J., et al
2022
Closest in time.
Dumitrescu, P.T., Bohnet, J.G., Gaebler, J.P., Hankin, A., Hayes, D., Kumar, A., Neyenhuis, B., Vasseur, R., Potter, A.C.: Dynamical topological phase realized in a trapped-ion quantum simulator. Nature 607
2022
Closest in time.
Huang, H.-Y., Broughton, M., Cotler, J., Chen, S., Li, J., Mohseni, M., Neven, H., Babbush, R., Kueng, R., Preskill, J., et al
2022
Closest in time.
2022
Closest in time.
Bharti, K., Cervera-Lierta, A., Kyaw, T.H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J.S., Menke, T., Mok, W.-K., Sim, S., Kwek, L.-C., Aspuru-Guzik, A.: Noisy intermediate-scale quantum algorithms. Rev. Mod. Phys. 94
2022
Closest in time.
Holmes, Z., Sharma, K., Cerezo, M., Coles, P.J.: Connecting ansatz expressibility to gradient magnitudes and barren plateaus. PRX Quantum 3
2022
Closest in time.
2022
Closest in time.
Schuld, M., Killoran, N.: Is quantum advantage the right goal for quantum machine learning? PRX Quantum 3
2022
Closest in time.
Huang, H.-Y., Broughton, M., Cotler, J., Chen, S., Li, J., Mohseni, M., Neven, H., Babbush, R., Kueng, R., Preskill, J., McClean, J.R.: Quantum advantage in learning from experiments. Science 376
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
Krunic, Z., Flöther, F.F., Seegan, G., Earnest-Noble, N.D., Shehab, O.: Quantum kernels for real-world predictions based on electronic health records. IEEE Transactions on Quantum Engineering 3
2022
Closest in time.
Trisovic, A., Lau, M.K., Pasquier, T., Crosas, M.: A large-scale study on research code quality and execution. Scientific Data 9
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
Cirq Developers: Cirq (2022). https://doi.org/10.5281/zenodo.6599601
2022
Closest in time.
Wierichs, D., Izaac, J., Wang, C., Lin, C.Y.-Y.: General parameter-shift rules for quantum gradients. Quantum 6
2022
Closest in time.
Heyraud, V., Li, Z., Denis, Z., Le Boité, A., Ciuti, C.: Noisy quantum kernel machines. Physical Review A 106
2022
Closest in time.