Список литературы / Bibliography#


Single trapped atom captures science photography competition's top prize. Accessed: 2021-10-05. URL: https://webarchive.nationalarchives.gov.uk/ukgwa/20200930161403/https://epsrc.ukri.org/newsevents/news/single-trapped-atom-captures-science-photography-competitions-top-prize/.


Varenna summer school on ion traps: quantum simulation, lecture i. Accessed: 2021-10-05. URL: https://iontrap.umd.edu/wp-content/uploads/2014/01/Lecture1_QSIM.pptx.


Shahnawaz Ahmed, Carlos Sánchez Muñoz, Franco Nori, and Anton Frisk Kockum. Quantum state tomography with conditional generative adversarial networks. Physical Review Letters, 127(14):140502, 2021. doi:https://doi.org/10.1103/PhysRevLett.127.140502.


Neculai Andrei. A diagonal quasi-newton updating method for unconstrained optimization. Numerical Algorithms, 81(2):575–590, 2019. doi:https://doi.org/10.1007/s11075-018-0562-7.


Walter Edwin Arnoldi. The principle of minimized iterations in the solution of the matrix eigenvalue problem. Quarterly of applied mathematics, 9(1):17–29, 1951. URL: https://www.ams.org/journals/qam/1951-09-01/S0033-569X-1951-42792-9/S0033-569X-1951-42792-9.pdf.


J. M. Arrazola, V. Bergholm, K. Brádler, T. R. Bromley, M. J. Collins, I. Dhand, A. Fumagalli, T. Gerrits, A. Goussev, L. G. Helt, J. Hundal, T. Isacsson, R. B. Israel, J. Izaac, S. Jahangiri, R. Janik, N. Killoran, S. P. Kumar, J. Lavoie, A. E. Lita, D. H. Mahler, M. Menotti, B. Morrison, S. W. Nam, L. Neuhaus, H. Y. Qi, N. Quesada, A. Repingon, K. K. Sabapathy, M. Schuld, D. Su, J. Swinarton, A. Száva, K. Tan, P. Tan, V. D. Vaidya, Z. Vernon, Z. Zabaneh, and Y. Zhang. Quantum circuits with many photons on a programmable nanophotonic chip. Nature, 591(7848):54–60, mar 2021. arXiv:2103.02109, doi:10.1038/s41586-021-03202-1.


Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando GSL Brandao, David A Buell, and others. Quantum supremacy using a programmable superconducting processor. Nature, 574(7779):505–510, 2019. doi:https://doi.org/10.1038/s41586-019-1666-5.


Francisco Barahona, Martin Grötschel, Michael Jünger, and Gerhard Reinelt. An application of combinatorial optimization to statistical physics and circuit layout design. Operations Research, 36(3):493–513, 1988.


Afrad Basheer, A. Afham, and Sandeep K. Goyal. Quantum $k$-nearest neighbors algorithm. 2021. arXiv:2003.09187.


Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, March 2004. ISBN 0521833787. URL: http://www.amazon.com/exec/obidos/redirect?tag=citeulike-20\&path=ASIN/0521833787.


Sergey Bravyi and Alexei Kitaev. Fermionic quantum computation. Annals of Physics, 298(1):210–226, 2002.


Michael Broughton, Guillaume Verdon, Trevor McCourt, Antonio J. Martinez, Jae Hyeon Yoo, Sergei V. Isakov, Philip Massey, Ramin Halavati, Murphy Yuezhen Niu, Alexander Zlokapa, Evan Peters, Owen Lockwood, Andrea Skolik, Sofiene Jerbi, Vedran Dunjko, Martin Leib, Michael Streif, David Von Dollen, Hongxiang Chen, Shuxiang Cao, Roeland Wiersema, Hsin-Yuan Huang, Jarrod R. McClean, Ryan Babbush, Sergio Boixo, Dave Bacon, Alan K. Ho, Hartmut Neven, and Masoud Mohseni. Tensorflow quantum: a software framework for quantum machine learning. 2021. URL: https://arxiv.org/abs/2003.02989, arXiv:2003.02989.


Giuseppe Carleo, Kenny Choo, Damian Hofmann, James ET Smith, Tom Westerhout, Fabien Alet, Emily J Davis, Stavros Efthymiou, Ivan Glasser, Sheng-Hsuan Lin, and others. Netket: a machine learning toolkit for many-body quantum systems. SoftwareX, 10:100311, 2019.


Giuseppe Carleo and Matthias Troyer. Solving the quantum many-body problem with artificial neural networks. Science, 355(6325):602–606, 2017. URL: https://arxiv.org/abs/1606.02318.


Andrew W. Cross, Lev S. Bishop, John A. Smolin, and Jay M. Gambetta. Open quantum assembly language. 2017. URL: https://arxiv.org/abs/1707.03429, arXiv:1707.03429.


Prasanna Date, Davis Arthur, and Lauren Pusey-Nazzaro. Qubo formulations for training machine learning models. Scientific Reports, 11:10029, May 2021. URL: https://doi.org/10.1038/s41598-021-89461-4, doi:10.1038/s41598-021-89461-4.


Prasanna Date, Robert Patton, and Thomas Potok. Efficiently embedding qubo problems on adiabatic quantum computers. Quantum Information Processing, 18:117, Mar 2019. URL: https://doi.org/10.1007/s11128-019-2236-3, doi:10.1007/s11128-019-2236-3.


Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. Mathematics for machine learning. Cambridge University Press, 2020. https://mml-book.github.io/book/mml-book.pdf.


Danial Dervovic, Mark Herbster, Peter Mountney, Simone Severini, Naïri Usher, and Leonard Wossnig. Quantum linear systems algorithms: a primer. 2018. arXiv:1802.08227.


Edward Farhi, Jeffrey Goldstone, and Sam Gutmann. A quantum approximate optimization algorithm. 2014. arXiv:1411.4028.


Edward Farhi, Jeffrey Goldstone, Sam Gutmann, and Michael Sipser. Quantum computation by adiabatic evolution. 2000. arXiv:0001106.


Fred Glover, Gary Kochenberger, and Yu Du. A tutorial on formulating and using qubo models. 2019. arXiv:1811.11538.


Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.


Peter Groszkowski and Jens Koch. Scqubits: a python package for superconducting qubits. arXiv e-prints, jul 2021. arXiv:2107.08552.


Aram W. Harrow, Avinatan Hassidim, and Seth Lloyd. Quantum algorithm for linear systems of equations. Physical Review Letters, Oct 2009. URL: https://arxiv.org/abs/0811.3171v3, doi:10.1103/physrevlett.103.150502.


W. K. Hastings. Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1):97–109, 04 1970. URL: https://doi.org/10.1093/biomet/57.1.97, arXiv:https://academic.oup.com/biomet/article-pdf/57/1/97/23940249/57-1-97.pdf, doi:10.1093/biomet/57.1.97.


Vojtěch Havlíček, Antonio D Córcoles, Kristan Temme, Aram W Harrow, Abhinav Kandala, Jerry M Chow, and Jay M Gambetta. Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747):209–212, Mar 2019. URL: https://arxiv.org/abs/1804.11326, doi:10.1038/s41586-019-0980-2.


Jan Hermann, Zeno Schätzle, and Frank Noé. Deep-neural-network solution of the electronic schrödinger equation. Nature Chemistry, 12(10):891–897, 2020. URL: https://www.nature.com/articles/s41557-020-0544-y.


Mohamed Hibat-Allah, Martin Ganahl, Lauren E Hayward, Roger G Melko, and Juan Carrasquilla. Recurrent neural network wave functions. Physical Review Research, 2(2):023358, 2020. URL: https://arxiv.org/abs/2002.02973.


He-Liang Huang, You-Wei Zhao, Tan Li, Feng-Guang Li, Yu-Tao Du, Xiang-Qun Fu, Shuo Zhang, Xiang Wang, and Wan-Su Bao. Homomorphic encryption experiments on ibm's cloud quantum computing platform. 2017. arXiv:1612.02886.


William Huggins, Piyush Patil, Bradley Mitchell, K Birgitta Whaley, and E Miles Stoudenmire. Towards quantum machine learning with tensor networks. Quantum Science and Technology, 4(2):024001, Jan 2019. URL: https://arxiv.org/abs/1803.11537, doi:10.1088/2058-9565/aaea94.


Ernst Ising. Beitrag zur theorie des ferromagnetismus. Zeitschrift für Physik, 31(1):253–258, 1925.


Robert Jastrow. Many-body problem with strong forces. Physical Review, 98(5):1479, 1955.


Tadashi Kadowaki and Hidetoshi Nishimori. Quantum annealing in the transverse ising model. Phys. Rev. E, 58:5355–5363, Nov 1998. URL: https://link.aps.org/doi/10.1103/PhysRevE.58.5355, doi:10.1103/PhysRevE.58.5355.


Abhinav Kandala, Antonio Mezzacapo, Kristan Temme, Maika Takita, Markus Brink, Jerry M Chow, and Jay M Gambetta. Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature, 549(7671):242–246, 2017. URL: https://www.nature.com/articles/nature23879, doi:10.1038/nature23879.


Jens Koch, Terri M Yu, Jay Gambetta, A A Houck, D I Schuster, J Majer, Alexandre Blais, M H Devoret, S M Girvin, and R J Schoelkopf. Charge-insensitive qubit design derived from the Cooper pair box. Physical Review A, 10 2007. doi:10.1103/physreva.76.042319.


P Krantz, M Kjaergaard, F Yan, T P Orlando, S Gustavsson, and W D Oliver. A Guide to Superconducting Qubits for New Quantum Information Engineers. Applied Physics Review, 2019. arXiv:1904.06560v1.


Cornelius Lanczos. An iteration method for the solution of the eigenvalue problem of linear differential and integral operators. Journal of Research of the National Bureau of Standards, 1950.


Ailsa H Land and Alison G Doig. An automatic method for solving discrete programming problems. In 50 Years of Integer Programming 1958-2008, pages 105–132. Springer, 2010.


Junde Li, Rasit Topaloglu, and Swaroop Ghosh. Quantum generative models for small molecule drug discovery. 2021. arXiv:2101.03438.


EY Loh Jr, JE Gubernatis, RT Scalettar, SR White, DJ Scalapino, and RL Sugar. Sign problem in the numerical simulation of many-electron systems. Physical Review B, 41(13):9301, 1990.


Chris Lomont. The hidden subgroup problem-review and open problems. arXiv preprint quant-ph/0411037, 2004.


Andrew Lucas. Ising formulations of many np problems. Frontiers in physics, 2:5, 2014. URL: https://arxiv.org/abs/1302.5843, doi:https://doi.org/10.3389/fphy.2014.00005.


Andrea Mari, Thomas R. Bromley, and Nathan Killoran. Estimating the gradient and higher-order derivatives on quantum hardware. Physical Review A, 103(1):012405, Jan 2021. URL: https://arxiv.org/abs/2008.06517, doi:10.1103/physreva.103.012405.


Kyle Mills, Michael Spanner, and Isaac Tamblyn. Deep learning and the schrödinger equation. Physical Review A, 96(4):042113, 2017. URL: https://arxiv.org/abs/1702.01361.


Kosuke Mitarai, Masahiro Kitagawa, and Keisuke Fujii. Quantum analog-digital conversion. Phys. Rev. A, 99:012301, Jan 2019. URL: https://link.aps.org/doi/10.1103/PhysRevA.99.012301, doi:10.1103/PhysRevA.99.012301.


Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii. Quantum circuit learning. Physical Review A, 98(3):032309, Sep 2018. URL: https://arxiv.org/abs/1803.00745, doi:10.1103/PhysRevA.98.032309.


Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of Machine Learning. Adaptive Computation and Machine Learning. MIT Press, Cambridge, MA, 2 edition, 2018. ISBN 978-0-262-03940-6.


Chetan Nayak, Steven H. Simon, Ady Stern, Michael Freedman, and Sankar Das Sarma. Non-abelian anyons and topological quantum computation. Reviews of Modern Physics, 80(3):1083–1159, Sep 2008. URL: http://dx.doi.org/10.1103/RevModPhys.80.1083, doi:10.1103/revmodphys.80.1083.


M. E. J. Newman. Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23):8577–8582, May 2006. URL: https://arxiv.org/abs/physics/0602124, doi:10.1073/pnas.0601602103.


Marco Roth, Marc Ganzhorn, Nikolaj Moll, Stefan Filipp, Gian Salis, and Sebastian Schmidt. Analysis of a parametrically driven exchange-type gate and a two-photon excitation gate between superconducting qubits. Physical Review A, 2017. doi:10.1103/PhysRevA.96.062323.


N. Samkharadze, G. Zheng, N. Kalhor, D. Brousse, A. Sammak, U. C. Mendes, A. Blais, G. Scappucci, and L. M. K. Vandersypen. Strong spin-photon coupling in silicon. Science, 359(6380):1123–1127, 2018. URL: https://www.science.org/doi/abs/10.1126/science.aar4054, doi:10.1126/science.aar4054.


Jacob T. Seeley, Martin J. Richard, and Peter J. Love. The bravyi-kitaev transformation for quantum computation of electronic structure. The Journal of chemical physics, 137(22):224109, 2012. URL: https://arxiv.org/abs/1208.5986.


David Sherrington and Scott Kirkpatrick. Solvable model of a spin-glass. Phys. Rev. Lett., 35:1792–1796, Dec 1975. URL: https://link.aps.org/doi/10.1103/PhysRevLett.35.1792, doi:10.1103/PhysRevLett.35.1792.


Semyon Sinchenko and Dmitry Bazhanov. The deep learning and statistical physics applications to the problems of combinatorial optimization. arXiv preprint arXiv:1911.10680, 2019. URL: https://arxiv.org/abs/1911.10680.


Robert S. Smith, Michael J. Curtis, and William J. Zeng. A practical quantum instruction set architecture. 2017. arXiv:1608.03355.


Stanislav Sobolevsky, Riccardo Campari, Alexander Belyi, and Carlo Ratti. General optimization technique for high-quality community detection in complex networks. Phys. Rev. E, 90:012811, Jul 2014. URL: https://link.aps.org/doi/10.1103/PhysRevE.90.012811, doi:10.1103/PhysRevE.90.012811.


Samuel A. Stein, Betis Baheri, Daniel Chen, Ying Mao, Qiang Guan, Ang Li, Bo Fang, and Shuai Xu. Qugan: a generative adversarial network through quantum states. 2021. arXiv:2010.09036.


Leonard Susskind and Art Friedman. Quantum mechanics: the theoretical minimum. Basic Books, 2014.


Yudai Suzuki, Hiroshi Yano, Qi Gao, Shumpei Uno, Tomoki Tanaka, Manato Akiyama, and Naoki Yamamoto. Analysis and synthesis of feature map for kernel-based quantum classifier. Quantum Machine Intelligence, 2(1):1–9, Jul 2020. URL: https://arxiv.org/abs/1906.10467, doi:10.1007/s42484-020-00020-y.


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