Quantum Computing in the NISQ era and beyond

John Preskill

Institute for Quantum Information and Matter and Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena CA 91125, USA

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Abstract

Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass the capabilities of today's classical digital computers, but noise in quantum gates will limit the size of quantum circuits that can be executed reliably. NISQ devices will be useful tools for exploring many-body quantum physics, and may have other useful applications, but the 100-qubit quantum computer will not change the world right away - we should regard it as a significant step toward the more powerful quantum technologies of the future. Quantum technologists should continue to strive for more accurate quantum gates and, eventually, fully fault-tolerant quantum computing.

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► References

[1] P. W. Shor, Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer, SIAM Rev. 41, 303-332 (1999), 10.1137/​S0036144598347011.
https:/​/​doi.org/​10.1137/​S0036144598347011

[2] A. P. Lund, M. J. Bremner, and T. C. Ralph, Quantum sampling problems, BosonSampling, and quantum supremacy, npj Quantum Information 3: 15 (2017), arXiv:1702.03061, 10.1038/​s41534-017-0018-2.
https:/​/​doi.org/​10.1038/​s41534-017-0018-2
arXiv:1702.03061

[3] A. W. Harrow and A. Montanaro, Quantum computational supremacy, Nature 549, 203-209 (2017), 10.1038/​nature23458.
https:/​/​doi.org/​10.1038/​nature23458

[4] S. P. Jordan, Quantum algorithm zoo, http:/​/​math.nist.gov/​quantum/​zoo/​.
http:/​/​math.nist.gov/​quantum/​zoo/​

[5] A. Montanaro, Quantum algorithms: an overview, npj Quantum Information, 15023 (2016), arXiv:1511.04206, 10.1038/​npjqi.2015.23.
https:/​/​doi.org/​10.1038/​npjqi.2015.23
arXiv:1511.04206

[6] L. Grover, Quantum mechanics helps in searching for a needle in a haystack, Phys. Rev. Lett. 79, 325 (1997), arXiv:quant-ph/​9706033, 10.1103/​PhysRevLett.79.325.
https:/​/​doi.org/​10.1103/​PhysRevLett.79.325
arXiv:quant-ph/9706033

[7] C. H. Bennett, E. Bernstein, G. Brassard, and U. Vazirani, Strengths and weaknesses of quantum computing, SIAM J. Comput. 26, 1510-1523 (1997), arXiv:quant-ph/​9701001, 10.1137/​S0097539796300933.
https:/​/​doi.org/​10.1137/​S0097539796300933
arXiv:quant-ph/9701001

[8] R. B. Laughlin and D. Pines, The theory of everything, PNAS 97, 28-31 (2000), 10.1073/​pnas.97.1.28.
https:/​/​doi.org/​10.1073/​pnas.97.1.28

[9] R. P. Feynman, Simulating physics with computers, Int. J. Theor. Physics 21, 467-488 (1982).

[10] D. Gottesman, An introduction to quantum error correction and fault-tolerant quantum computation, Proceedings of Symposia in Applied Matthematics 68 (2010), arXiv:0904.2557.
arXiv:0904.2557

[11] S. Boixo, S. V. Isakov, V. N. Smelyansky, R. Babbush, N. Ding, Z. Jiang, M. J. Bremner, J. M. Martinis, and H. Neven, Characterizing quantum supremacy in near-term devices, Nature Physics 14, 595-600 (2018), arXiv:1608.00263 (2016), 10.1038/​s41567-018-0124-x.
https:/​/​doi.org/​10.1038/​s41567-018-0124-x
arXiv:1608.00263

[12] S. Aaronson and L. Chen, Complexity-theoretic foundations of quantum supremacy experiments, arXiv:1612.05903 (2017).
arXiv:1612.05903

[13] E. Pednault, J. A. Gunnels, G. Nannicini, L. Horesh, T. Magerlein, E. Solomonik, and R. Wisnieff, Breaking the 49-qubit barrier in the simulation of quantum circuits, arXiv:1710.05867 (2017).
arXiv:1710.05867

[14] C. J. Ballance, T. P. Harty, N. M. Linke, M. A. Sepiol, and D. M. Lucas, High-fidelity quantum logic gates using trapped-ion hyperfine qubits, Phys. Rev. Lett. 117, 060504 (2016), arXiv:1512.04600, 10.1103/​PhysRevLett.117.060504.
https:/​/​doi.org/​10.1103/​PhysRevLett.117.060504
arXiv:1512.04600

[15] R. Barends, J. Kelly, A. Megrant, A. Veitia, D. Sank, E. Jeffrey, T. C. White, J. Mutus, A. G. Fowler, B. Campbell, Y. Chen, Z. Chen, B. Chiaro, A. Dunsworth, C. Neill, P. O'Malley, P. Roushan, A. Vainsencher, J. Wenner, A. N. Korotkov, A. N. Cleland, and J. M. Martinis, Superconducting quantum circuits at the surface code threshold for fault tolerance, Nature 508, 500-503 (2014), arXiv:1402.4848, 10.1038/​nature13171.
https:/​/​doi.org/​10.1038/​nature13171
arXiv:1402.4848

[16] D. J. Bernstein, J. Buchmann, E. Dahmen, editors, Post-Quantum Cryptography, Springer (2009), 10.1007/​978-3-540-88702-7.
https:/​/​doi.org/​10.1007/​978-3-540-88702-7

[17] R. Alléaume, C. Branciard, J. Bouda, T. Debuisschert, M. Dianati, N. Gisin, M. Godfrey, P. Grangier, T. Länger, N. Lütkenhaus, C. Monyk, P. Painchault, M. Peev, A. Poppe, T. Pornin, J. Rarity, R. Renner, G. Ribordy, M. Riguidel, L. Salvail, A. Shields, H. Weinfurter, and A. Zeilinger, Using quantum key distribution for cryptographic purposes: a survey, Theoretical Computer Science 560, 62-81 (2014), arXiv:quant-ph/​0701168, 10.1016/​j.tcs.2014.09.018.
https:/​/​doi.org/​10.1016/​j.tcs.2014.09.018
arXiv:quant-ph/0701168

[18] S. Muralidharan, L. Li, J. Kim, N Lütkenhaus, M. D. Lukin, and L. Jiang, Efficient long distance quantum communication, Scientific Reports 6, 20463 (2016), arXiv:1509.08435, 10.1038/​srep20463.
https:/​/​doi.org/​10.1038/​srep20463
arXiv:1509.08435

[19] P. Bierhorst, E. Knill, S. Glancy, Y. Zhang, A. Mink, S. Jordan, A. Rommal, Y.-K. Liu, B. Christensen, S. W. Nam, M. J. Stevens, and L. K. Shalm, Experimentally generated randomness certified by the impossibility of superluminal signals, Nature 556, 223-226 (2018), arXiv:1803.06219, 10.1038/​s41586-018-0019-0.
https:/​/​doi.org/​10.1038/​s41586-018-0019-0
arXiv:1803.06219

[20] Z. Brakerski, P. Christiano, U. Mahadev, U. Vazirani, and T. Vidick, Certifiable randomness from a single quantum device, arXiv:1804.00640 (2018).
arXiv:1804.00640

[21] C. L. Degen, F. Reinhard, and P. Cappellaro, Quantum sensing, Rev. Mod. Phys. 89, 035002 (2017), arXiv:1611.04691, 10.1103/​RevModPhys.89.035002.
https:/​/​doi.org/​10.1103/​RevModPhys.89.035002
arXiv:1611.04691

[22] J. Preskill, Quantum computing and the entanglement frontier, 25th Solvay Conference on Physics (2011), arXiv:1203.5813.
arXiv:1203.5813

[23] S. Khot, Hardness of approximation, Proceedings of the International Congress of Mathematicians (2014).

[24] E. Farhi, J. Goldstone, and S. Gutmann, A quantum approximate optimization algorithm, arXiv:1411.4028 (2014).
arXiv:1411.4028

[25] J. R. McClean, J. Romero, R. Babbush, and A. Aspuru-Guzik, The theory of variational hybrid quantum-classical algorithms, New J. Phys. 18, 023023 (2016), arXiv:1509.04279, 10.1038/​ncomms5213.
https:/​/​doi.org/​10.1038/​ncomms5213
arXiv:1509.04279

[26] D. A. Spielman and S.-H. Teng, Smoothed analysis of algorithms: why the simplex algorithm usually takes polynomial time, Journal of the ACM 51, 385-463 (2004), arXiv:cs/​0111050, 10.1145/​990308.990310.
https:/​/​doi.org/​10.1145/​990308.990310
arXiv:cs/0111050

[27] Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature 521, 436-444 (2015), 10.1038/​nature14539.
https:/​/​doi.org/​10.1038/​nature14539

[28] T. F. Rønnow, Z. Wang, J. Job, S. Boixo, S. V. Isakov, D. Wecker, J. M. Martinis, D. A. Lidar, and M. Troyer, Defining and detecting quantum speedup, Science 345, 420-424 (2014), 10.1126/​science.1252319.
https:/​/​doi.org/​10.1126/​science.1252319

[29] S. Mandrà, H. G. Katzgraber, and C. Thomas, The pitfalls of planar spin-glass benchmarks: raising the bar for quantum annealers (again), Quantum Sci. Technol. 2, 038501 (2017), arXiv:1703.00622, 10.1088/​2058-9565/​aa7877.
https:/​/​doi.org/​10.1088/​2058-9565/​aa7877
arXiv:1703.00622

[30] T. Albash and D. A. Lidar, Adiabatic quantum computing, Rev. Mod. Phys. 90, 015002 (2018), arXiv:1611.04471, 10.1103/​RevModPhys.90.015002.
https:/​/​doi.org/​10.1103/​RevModPhys.90.015002
arXiv:1611.04471

[31] D. Aharonov, W. van Dam, J. Kempe, Z. Landau, S. Lloyd, and O. Regev, Adiabatic quantum computation is equivalent to standard quantum computation, SIAM Rev. 50, 755-787 (2008), arXiv:quant-ph/​0405098.
arXiv:quant-ph/0405098

[32] S. Bravyi, D. DiVincenzo, R. I. Oliveira, and B. M. Terhal, The complexity of stoquastic local Hamiltonian problems, Quant. Inf. Comp. 8, 0361-0385 (2008), arXiv:quant-ph/​0606140.
arXiv:quant-ph/0606140

[33] M. Jarret, S. P. Jordan, and B. Lackey, Adiabatic optimization versus diffusion Monte Carlo, Phys. Rev. A 94, 042318 (2016), arXiv:1607.03389, 10.1103/​PhysRevA.94.042318.
https:/​/​doi.org/​10.1103/​PhysRevA.94.042318
arXiv:1607.03389

[34] A. D. King, J. Carrasquilla, I. Ozfidan, J. Raymond, E. Andriyash, A. Berkley, M. Reis, T. M. Lanting, R. Harris, G. Poulin-Lamarre, A. Yu. Smirnov, C. Rich, F. Altomare, P. Bunyk, J. Whittaker, L. Swenson, E. Hoskinson, Y. Sato, M. Volkmann, E. Ladizinsky, M. Johnson, J. Hilton, and M. H. Amin, Observation of topological phenomena in a programmable lattice of 1,800 qubits, arXiv:1803.02047 (2018).
arXiv:1803.02047

[35] I. H. Kim, Noise-resilient preparation of quantum many-body ground states, arXiv:1703.00032 (2017).
arXiv:1703.00032

[36] I. H. Kim and B. Swingle, Robust entanglement renormalization on a noisy quantum computer, arXiv:1711.07500 (2017).
arXiv:1711.07500

[37] J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, Quantum machine learning, Nature 549, 195-202 (2017), arXiv:1611.09347, 10.1038/​nature23474.
https:/​/​doi.org/​10.1038/​nature23474
arXiv:1611.09347

[38] S. Aaronson, Read the fine print, Nature Physics 11, 291-293 (2015), 10.1038/​nphys3272.
https:/​/​doi.org/​10.1038/​nphys3272

[39] X. Gao, Z. Zhang, and L. Duan, An efficient quantum algorithm for generative machine learning, arXiv:1711.02038 (2017).
arXiv:1711.02038

[40] A. W. Harrow, A. Hassidim, and S. Lloyd, Quantum algorithm for linear systems of equations, Phys. Rev. Lett. 103, 150502 (2009), arXiv:0811.3171, 10.1103/​PhysRevLett.103.150502.
https:/​/​doi.org/​10.1103/​PhysRevLett.103.150502
arXiv:0811.3171

[41] B. D. Clader, B. C. Jacobs, and C. R. Sprouse, Preconditioned quantum linear system algorithm, Phys. Rev. Lett. 110, 250504 (2013), arXiv:1301.2340, 10.1103/​PhysRevLett.110.250504.
https:/​/​doi.org/​10.1103/​PhysRevLett.110.250504
arXiv:1301.2340

[42] A. Montanaro and S. Pallister, Quantum algorithms and the finite element method, Phys. Rev. A 93, 032324 (2016), arXiv:1512.05903, 10.1103/​PhysRevA.93.032324.
https:/​/​doi.org/​10.1103/​PhysRevA.93.032324
arXiv:1512.05903

[43] P. C. S. Costa, S. Jordan, and A. Ostrander, Quantum algorithm for simulating the wave equation, arXiv:1711.05394 (2017).
arXiv:1711.05394

[44] I. Kerenidis and A. Prakash, Quantum recommendation systems, arXiv:1603.08675 (2016).
arXiv:1603.08675

[45] E. Tang, A quantum-inspired classical algorithm for recommendation systems, Electronic Colloquium on Computational Complexity, TR18-12 (2018).

[46] F. G. S. L. Brandão and K. Svore, Quantum speed-ups for semidefinite programming, Proceedings of FOCS 2017, arXiv:1609.05537 (2017).
arXiv:1609.05537

[47] F. G. S. L. Brandão, A. Kalev, T. Li, C. Y.-Y. Lin, K. M. Svore, and X. Wu, Exponential quantum speed-ups for semidefinite programming with applications to quantum learning, arXiv:1710.02581 (2017).
arXiv:1710.02581

[48] M. Reiher, N. Wiebe, K. M. Svore, D. Wecker, and M. Troyer, Elucidating reaction mechanisms on quantum computers, PNAS 117, 7555-7560 (2017), arXiv:1605.03590, 10.1073/​pnas.1619152114.
https:/​/​doi.org/​10.1073/​pnas.1619152114
arXiv:1605.03590

[49] D. Wecker, M. B. Hastings, N. Wiebe, B. K. Clark, C. Nayak, and M. Troyer, Solving strongly correlated electron models on a quantum computer, Phys. Rev. A 92, 062310 (2015), arXiv:1506.05135, 10.1103/​PhysRevA.92.062318.
https:/​/​doi.org/​10.1103/​PhysRevA.92.062318
arXiv:1506.05135

[50] J. Olson, Y. Cao, J. Romero, P. Johnson, P.-L. Dallaire-Demers, N. Sawaya, P. Narang, I. Kivlichan, M. Wasielewski, A. Aspuru-Guzik, Quantum information and computation for chemistry, NSF Workshop Report, arXiv:1706.05413 (2017).
arXiv:1706.05413

[51] H. Bernien, S. Schwartz, A. Keesling, H. Levine, A. Omran, H. Pichler, S. Choi, A. S. Zibrov, M. Endres, M. Greiner, V Vuletić, and M. D. Lukin, Probing many-body dynamics on a 51-atom quantum simulator, Nature 551, 579-584 (2017), arXiv:1707.04344, 10.1038/​nature24622.
https:/​/​doi.org/​10.1038/​nature24622
arXiv:1707.04344

[52] J. Zhang, G. Pagano, P. W. Hess, A. Kyprianidis, P. Becker, H. Kaplan, A. V. Gorshkov, Z.-X. Gong, and C. Monroe, Observation of a many-body dynamical phase transition with a 53-qubit quantum simulator, arXiv:1708.01044 (2017), 10.1038/​nature24654.
https:/​/​doi.org/​10.1038/​nature24654
arXiv:1708.01044

[53] E. T. Campbell, B. M. Terhal, and C. Vuillot, The steep road towards robust and universal quantum computation, arXiv:1612.07330 (2016).
arXiv:1612.07330

[54] J. J. Wallman and J. Emerson, Noise tailoring for scalable quantum computation via randomized compiling, Phys. Rev. A 94, 052325 (2016), arXiv:1512:01098, 10.1103/​PhysRevA.94.052325.
https:/​/​doi.org/​10.1103/​PhysRevA.94.052325
arXiv:1512

[55] J. Combes, C. Granade, C. Ferrie, and S. T. Flammia, Logical randomized benchmarking, arXiv:1702.03688 (2017).
arXiv:1702.03688

[56] A. G. Fowler, M. Mariantoni, J. M. Martinis, and A. N. Cleland, Surface codes: towards practical large-scale quantum computation, Phys. Rev. A 86, 032324 (2012), arXiv:1208.0928, 10.1103/​PhysRevA.86.032324.
https:/​/​doi.org/​10.1103/​PhysRevA.86.032324
arXiv:1208.0928

[57] S. Das Sarma, M. Freedman, and C. Nayak, Majorana zero modes and topological quantum computation, npj Quantum Information 1, 15001 (2015), arXiv:1501.02813, 10.1038/​npjqi.2015.1.
https:/​/​doi.org/​10.1038/​npjqi.2015.1
arXiv:1501.02813

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[1] Andrew D. King, Juan Carrasquilla, Jack Raymond, Isil Ozfidan, Evgeny Andriyash, Andrew Berkley, Mauricio Reis, Trevor Lanting, Richard Harris, Fabio Altomare, Kelly Boothby, Paul I. Bunyk, Colin Enderud, Alexandre Fréchette, Emile Hoskinson, Nicolas Ladizinsky, Travis Oh, Gabriel Poulin-Lamarre, Christopher Rich, Yuki Sato, Anatoly Yu. Smirnov, Loren J. Swenson, Mark H. Volkmann, Jed Whittaker, Jason Yao, Eric Ladizinsky, Mark W. Johnson, Jeremy Hilton, and Mohammad H. Amin, "Observation of topological phenomena in a programmable lattice of 1,800 qubits", Nature 560 7719, 456 (2018).

[2] Abhinav Kandala, Kristan Temme, Antonio D. Córcoles, Antonio Mezzacapo, Jerry M. Chow, and Jay M. Gambetta, "Error mitigation extends the computational reach of a noisy quantum processor", Nature 567 7749, 491 (2019).

[3] 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 (2019).

[4] Victor V. Albert, Kyungjoo Noh, Kasper Duivenvoorden, Dylan J. Young, R. T. Brierley, Philip Reinhold, Christophe Vuillot, Linshu Li, Chao Shen, S. M. Girvin, Barbara M. Terhal, and Liang Jiang, "Performance and structure of single-mode bosonic codes", Physical Review A 97 3, 032346 (2018).

[5] Jianxin Chen, Fang Zhang, Cupjin Huang, Michael Newman, and Yaoyun Shi, "Classical Simulation of Intermediate-Size Quantum Circuits", arXiv:1805.01450.

[6] Edward Grant, Marcello Benedetti, Shuxiang Cao, Andrew Hallam, Joshua Lockhart, Vid Stojevic, Andrew G. Green, and Simone Severini, "Hierarchical quantum classifiers", npj Quantum Information 4, 65 (2018).

[7] Panagiotis Kl. Barkoutsos, Jerome F. Gonthier, Igor Sokolov, Nikolaj Moll, Gian Salis, Andreas Fuhrer, Marc Ganzhorn, Daniel J. Egger, Matthias Troyer, Antonio Mezzacapo, Stefan Filipp, and Ivano Tavernelli, "Quantum algorithms for electronic structure calculations: Particle-hole Hamiltonian and optimized wave-function expansions", Physical Review A 98 2, 022322 (2018).

[8] Aram Harrow and John Napp, "Low-depth gradient measurements can improve convergence in variational hybrid quantum-classical algorithms", arXiv:1901.05374.

[9] Sam McArdle, Suguru Endo, Alan Aspuru-Guzik, Simon Benjamin, and Xiao Yuan, "Quantum computational chemistry", arXiv:1808.10402.

[10] Seth Lloyd and Christian Weedbrook, "Quantum Generative Adversarial Learning", Physical Review Letters 121 4, 040502 (2018).

[11] Tameem Albash and Daniel A. Lidar, "Demonstration of a Scaling Advantage for a Quantum Annealer over Simulated Annealing", Physical Review X 8 3, 031016 (2018).

[12] Jonathan Romero and Alan Aspuru-Guzik, "Variational quantum generators: Generative adversarial quantum machine learning for continuous distributions", arXiv:1901.00848.

[13] Guillaume Verdon, Michael Broughton, Jarrod R. McClean, Kevin J. Sung, Ryan Babbush, Zhang Jiang, Hartmut Neven, and Masoud Mohseni, "Learning to learn with quantum neural networks via classical neural networks", arXiv:1907.05415.

[14] Yudong Cao, Jonathan Romero, Jonathan P. Olson, Matthias Degroote, Peter D. Johnson, Mária Kieferová, Ian D. Kivlichan, Tim Menke, Borja Peropadre, Nicolas P. D. Sawaya, Sukin Sim, Libor Veis, and Alán Aspuru-Guzik, "Quantum Chemistry in the Age of Quantum Computing", arXiv:1812.09976.

[15] Guillaume Verdon, Jason Pye, and Michael Broughton, "A Universal Training Algorithm for Quantum Deep Learning", arXiv:1806.09729.

[16] Pierre-Luc Dallaire-Demers and Nathan Killoran, "Quantum generative adversarial networks", Physical Review A 98 1, 012324 (2018).

[17] Swamit S. Tannu and Moinuddin K. Qureshi, "A Case for Variability-Aware Policies for NISQ-Era Quantum Computers", arXiv:1805.10224.

[18] Sergey Bravyi, Dan Browne, Padraic Calpin, Earl Campbell, David Gosset, and Mark Howard, "Simulation of quantum circuits by low-rank stabilizer decompositions", arXiv:1808.00128.

[19] J. Preskill, "Simulating quantum field theory with a quantum computer", The 36th Annual International Symposium on Lattice Field Theory. 22-28 July 24 (2018).

[20] Zhang Jiang, Kevin J. Sung, Kostyantyn Kechedzhi, Vadim N. Smelyanskiy, and Sergio Boixo, "Quantum Algorithms to Simulate Many-Body Physics of Correlated Fermions", Physical Review Applied 9 4, 044036 (2018).

[21] Zhang Jiang, Jarrod McClean, Ryan Babbush, and Hartmut Neven, "Majorana Loop Stabilizer Codes for Error Mitigation in Fermionic Quantum Simulations", Physical Review Applied 12 6, 064041 (2019).

[22] Shin Nishio, Yulu Pan, Takahiko Satoh, Hideharu Amano, and Rodney Van Meter, "Extracting Success from IBM's 20-Qubit Machines Using Error-Aware Compilation", arXiv:1903.10963.

[23] Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, and Dacheng Tao, "The Expressive Power of Parameterized Quantum Circuits", arXiv:1810.11922.

[24] Brian Swingle and Nicole Yunger Halpern, "Resilience of scrambling measurements", Physical Review A 97 6, 062113 (2018).

[25] Jiayin Chen and Hendra I. Nurdin, "Learning nonlinear input-output maps with dissipative quantum systems", Quantum Information Processing 18 7, 198 (2019).

[26] Gushu Li, Yufei Ding, and Yuan Xie, "Tackling the Qubit Mapping Problem for NISQ-Era Quantum Devices", arXiv:1809.02573.

[27] Ling Hu, Shu-Hao Wu, Weizhou Cai, Yuwei Ma, Xianghao Mu, Yuan Xu, Haiyan Wang, Yipu Song, Dong-Ling Deng, Chang-Ling Zou, and Luyan Sun, "Quantum generative adversarial learning in a superconducting quantum circuit", Science Advances 5 1, eaav2761 (2019).

[28] Mark Fingerhuth, Tomáš Babej, and Christopher Ing, "A quantum alternating operator ansatz with hard and soft constraints for lattice protein folding", arXiv:1810.13411.

[29] Eric R. Anschuetz, Jonathan P. Olson, Alán Aspuru-Guzik, and Yudong Cao, "Variational Quantum Factoring", arXiv:1808.08927.

[30] Ian C. Cloët, Matthew R. Dietrich, John Arrington, Alexei Bazavov, Michael Bishof, Adam Freese, Alexey V. Gorshkov, Anna Grassellino, Kawtar Hafidi, Zubin Jacob, Michael McGuigan, Yannick Meurice, Zein-Eddine Meziani, Peter Mueller, Christine Muschik, James Osborn, Matthew Otten, Peter Petreczky, Tomas Polakovic, Alan Poon, Raphael Pooser, Alessandro Roggero, Mark Saffman, Brent VanDevender, Jiehang Zhang, and Erez Zohar, "Opportunities for Nuclear Physics & Quantum Information Science", arXiv:1903.05453.

[31] Maria Schuld and Nathan Killoran, "Quantum machine learning in feature Hilbert spaces", arXiv:1803.07128.

[32] Cupjin Huang, Michael Newman, and Mario Szegedy, "Explicit lower bounds on strong quantum simulation", arXiv:1804.10368.

[33] Jacob Biamonte, "Universal Variational Quantum Computation", arXiv:1903.04500.

[34] Neal Solmeyer, Norbert M. Linke, Caroline Figgatt, Kevin A. Landsman, Radhakrishnan Balu, George Siopsis, and C. Monroe, "Demonstration of a Bayesian quantum game on an ion-trap quantum computer", Quantum Science and Technology 3 4, 045002 (2018).

[35] Juan Miguel Arrazola, Thomas R. Bromley, and Patrick Rebentrost, "Quantum approximate optimization with Gaussian boson sampling", Physical Review A 98 1, 012322 (2018).

[36] A. Garcia-Saez and J. I. Latorre, "Addressing hard classical problems with Adiabatically Assisted Variational Quantum Eigensolvers", arXiv:1806.02287.

[37] Tameem Albash, Victor Martin-Mayor, and Itay Hen, "Analog errors in Ising machines", Quantum Science and Technology 4 2, 02LT03 (2019).

[38] James Stokes and John Terilla, "Probabilistic Modeling with Matrix Product States", Entropy 21 12, 1236 (2019).

[39] Ali Mortezapour and Rosario Lo Franco, "Protecting quantum resources via frequency modulation of qubits in leaky cavities", Scientific Reports 8, 14304 (2018).

[40] C. M. Wilson, J. S. Otterbach, N. Tezak, R. S. Smith, A. M. Polloreno, Peter J. Karalekas, S. Heidel, M. Sohaib Alam, G. E. Crooks, and M. P. da Silva, "Quantum Kitchen Sinks: An algorithm for machine learning on near-term quantum computers", arXiv:1806.08321.

[41] K. Bertels, A. Sarkar, A. A. Mouedenne, T. Hubregtsen, A. Yadav, A. Krol, and I. Ashraf, "Quantum Computer Architecture: Towards Full-Stack Quantum Accelerators", arXiv:1903.09575.

[42] Bryan O'Gorman, William J. Huggins, Eleanor G. Rieffel, and K. Birgitta Whaley, "Generalized swap networks for near-term quantum computing", arXiv:1905.05118.

[43] Nai-Hui Chia, Tongyang Li, Han-Hsuan Lin, and Chunhao Wang, "Quantum-inspired sublinear algorithm for solving low-rank semidefinite programming", arXiv:1901.03254.

[44] Sergey Novikov, Robert Hinkey, Steven Disseler, James I. Basham, Tameem Albash, Andrew Risinger, David Ferguson, Daniel A. Lidar, and Kenneth M. Zick, "Exploring More-Coherent Quantum Annealing", arXiv:1809.04485.

[45] Eyal Bairey, Itai Arad, and Netanel H. Lindner, "Learning a local Hamiltonian from local measurements", arXiv:1807.04564.

[46] Ning Bao and Junyu Liu, "Quantum algorithms for conformal bootstrap", Nuclear Physics B 946, 114702 (2019).

[47] Xun Gao and Luming Duan, "Efficient classical simulation of noisy quantum computation", arXiv:1810.03176.

[48] Wolfgang Lechner, "Quantum Approximate Optimization with Parallelizable Gates", arXiv:1802.01157.

[49] Kostyantyn Kechedzhi, Vadim Smelyanskiy, Jarrod R. McClean, Vasil S. Denchev, Masoud Mohseni, Sergei Isakov, Sergio Boixo, Boris Altshuler, and Hartmut Neven, "Efficient population transfer via non-ergodic extended states in quantum spin glass", arXiv:1807.04792.

[50] Alexander McCaskey, Eugene Dumitrescu, Mengsu Chen, Dmitry Lyakh, and Travis Humble, "Validating quantum-classical programming models with tensor network simulations", PLoS ONE 13 12, e0206704 (2018).

[51] Marina Radulaski, Jingyuan Linda Zhang, Yan-Kai Tzeng, Konstantinos G. Lagoudakis, Hitoshi Ishiwata, Constantin Dory, Kevin A. Fischer, Yousif A. Kelaita, Shuo Sun, Peter C. Maurer, Kassem Alassaad, Gabriel Ferro, Zhi-Xun Shen, Nicholas Melosh, Steven Chu, and Jelena Vučković, "Nanodiamond integration with photonic devices", arXiv:1610.03183.

[52] Seth Lloyd and Reevu Maity, "Efficient implementation of unitary transformations", arXiv:1901.03431.

[53] Ramis Movassagh, "Efficient unitary paths and quantum computational supremacy: A proof of average-case hardness of Random Circuit Sampling", arXiv:1810.04681.

[54] Mohammad H. Ansari, "Exact quantization of superconducting circuits", arXiv:1807.00792.

[55] Ramis Movassagh, "Quantum supremacy and random circuits", arXiv:1909.06210.

[56] Tongyang Li, Shouvanik Chakrabarti, and Xiaodi Wu, "Sublinear quantum algorithms for training linear and kernel-based classifiers", arXiv:1904.02276.

[57] Kentaro Heya, Yasunari Suzuki, Yasunobu Nakamura, and Keisuke Fujii, "Variational Quantum Gate Optimization", arXiv:1810.12745.

[58] Gavin E. Crooks, "Gradients of parameterized quantum gates using the parameter-shift rule and gate decomposition", arXiv:1905.13311.

[59] Jin-Guo Liu and Lei Wang, "Differentiable Learning of Quantum Circuit Born Machine", arXiv:1804.04168.

[60] Juan Carrasquilla, Giacomo Torlai, Roger G. Melko, and Leandro Aolita, "Reconstructing quantum states with generative models", arXiv:1810.10584.

[61] X. Fu, L. Riesebos, M. A. Rol, J. van Straten, J. van Someren, N. Khammassi, I. Ashraf, R. F. L. Vermeulen, V. Newsum, K. K. L. Loh, J. C. de Sterke, W. J. Vlothuizen, R. N. Schouten, C. G. Almudever, L. DiCarlo, and K. Bertels, "eQASM: An Executable Quantum Instruction Set Architecture", arXiv:1808.02449.

[62] Aniruddha Bapat and Stephen Jordan, "Bang-bang control as a design principle for classical and quantum optimization algorithms", arXiv:1812.02746.

[63] Valery Shchesnovich, "On the classical complexity of sampling from quantum interference of indistinguishable bosons", arXiv:1904.02013.

[64] Javier Gil Vidal and Dirk Oliver Theis, "Calculus on parameterized quantum circuits", arXiv:1812.06323.

[65] Alwin Zulehner and Robert Wille, "Compiling SU(4) Quantum Circuits to IBM QX Architectures", arXiv:1808.05661.

[66] Zhong-Xiao Man, Yun-Jie Xia, and Rosario Lo Franco, "Temperature effects on quantum non-Markovianity via collision models", Physical Review A 97 6, 062104 (2018).

[67] Kazuki Ikeda, Yuma Nakamura, and Travis S. Humble, "Application of Quantum Annealing to Nurse Scheduling Problem", Scientific Reports 9, 12837 (2019).

[68] Suguru Endo, Qi Zhao, Ying Li, Simon Benjamin, and Xiao Yuan, "Mitigating algorithmic errors in Hamiltonian simulation", arXiv:1808.03623.

[69] Xavier Waintal, "What determines the ultimate precision of a quantum computer?", arXiv:1702.07688.

[70] Valery Shchesnovich, "Distinguishing noisy boson sampling from classical simulations", arXiv:1905.11458.

[71] Mingxia Huo and Ying Li, "Self-consistent tomography of temporally correlated errors", arXiv:1811.02734.

[72] Prakash Murali, Norbert Matthias Linke, Margaret Martonosi, Ali Javadi Abhari, Nhung Hong Nguyen, and Cinthia Huerta Alderete, "Full-Stack, Real-System Quantum Computer Studies: Architectural Comparisons and Design Insights", arXiv:1905.11349.

[73] David P. Franke, James S. Clarke, Lieven M. K. Vandersypen, and Menno Veldhorst, "Rent's rule and extensibility in quantum computing", arXiv:1806.02145.

[74] Dorit Aharonov and Leo Zhou, "Hamiltonian sparsification and gap-simulations", arXiv:1804.11084.

[75] Johannes S. Otterbach, "Optimizing Variational Quantum Circuits using Evolution Strategies", arXiv:1806.04344.

[76] Yuxuan Du, Min-Hsiu Hsieh, and Dacheng Tao, "Efficient Online Quantum Generative Adversarial Learning Algorithms with Applications", arXiv:1904.09602.

[77] Ruslan Shaydulin, Hayato Ushijima-Mwesigwa, Ilya Safro, Susan Mniszewski, and Yuri Alexeev, "Network Community Detection On Small Quantum Computers", arXiv:1810.12484.

[78] Frederic Bapst, Wahid Bhimji, Paolo Calafiura, Heather Gray, Wim Lavrijsen, and Lucy Linder, "A pattern recognition algorithm for quantum annealers", arXiv:1902.08324.

[79] Siddhartha Das, "Bipartite Quantum Interactions: Entangling and Information Processing Abilities", arXiv:1901.05895.

[80] Omar Shehab, Isaac H. Kim, Nhung H. Nguyen, Kevin Landsman, Cinthia H. Alderete, Daiwei Zhu, C. Monroe, and Norbert M. Linke, "Noise reduction using past causal cones in variational quantum algorithms", arXiv:1906.00476.

[81] Freek Witteveen, Volkher Scholz, Brian Swingle, and Michael Walter, "Quantum circuit approximations and entanglement renormalization for the Dirac field in 1+1 dimensions", arXiv:1905.08821.

[82] Cristian S. Calude and Elena Calude, "The Road to Quantum Computational Supremacy", arXiv:1712.01356.

[83] Prakash Murali, Ali Javadi-Abhari, Frederic T. Chong, and Margaret Martonosi, "Formal Constraint-based Compilation for Noisy Intermediate-Scale Quantum Systems", arXiv:1903.03276.

[84] Salonik Resch and Ulya R. Karpuzcu, "Quantum Computing: An Overview Across the System Stack", arXiv:1905.07240.

[85] F. Tacchino, A. Chiesa, M. D. LaHaye, I. Tavernelli, S. Carretta, and D. Gerace, "Digital Quantum Simulations of Spin Models on Hybrid Platform and Near-Term Quantum Processors", arXiv:1902.04971.

[86] Ryan Bennink, Ajay Jasra, Kody J. H. Law, and Pavel Lougovski, "Estimation and uncertainty quantification for the output from quantum simulators", arXiv:1903.02964.

[87] Shusen Liu, Yinan Li, and Runyao Duan, "Distinguishing Unitary Gates on the IBM Quantum Processor", arXiv:1807.00429.

[88] V. O. Shkolnikov and Guido Burkard, "Effective Hamiltonian theory of the geometric evolution of quantum systems", arXiv:1810.00193.

[89] Ruslan Shaydulin, Caleb Thomas, and Paige Rodeghero, "Making Quantum Computing Open: Lessons from Open-Source Projects", arXiv:1902.00991.

[90] Taewan Kim and Byung-Soo Choi, "Efficient decomposition methods for controlled-R<SUB>n</SUB> using a single ancillary qubit", Scientific Reports 8, 5445 (2018).

[91] V. E. Zobov and I. S. Pichkovskiy, "Sequences of selective rotation operators to engineer interactions for quantum annealing on three qutrits", International Conference on Micro- and Nano-Electronics 2018 11022, 110222V (2019).

[92] Damian S. Steiger, Thomas Häner, and Matthias Troyer, "Advantages of a modular high-level quantum programming framework", arXiv:1806.01861.

[93] Ruslan Shaydulin, Ilya Safro, and Jeffrey Larson, "Multistart Methods for Quantum Approximate Optimization", arXiv:1905.08768.

[94] Patrick Rall, "Simulating Quantum Circuits by Shuffling Paulis", arXiv:1804.05404.

[95] Yongshan Ding, Adam Holmes, Ali Javadi-Abhari, Diana Franklin, Margaret Martonosi, and Frederic T. Chong, "Magic-State Functional Units: Mapping and Scheduling Multi-Level Distillation Circuits for Fault-Tolerant Quantum Architectures", arXiv:1809.01302.

[96] Stefan Krastanov, Victor V. Albert, and Liang Jiang, "Optimized Entanglement Purification", arXiv:1712.09762.

[97] Gushu Li, Yufei Ding, and Yuan Xie, "SANQ: A Simulation Framework for Architecting Noisy Intermediate-Scale Quantum Computing System", arXiv:1904.11590.

[98] Alexandru Paler, "SurfBraid: A concept tool for preparing and resource estimating quantum circuits protected by the surface code", arXiv:1902.02417.

[99] Adam Smith, Bernhard Jobst, Andrew G. Green, and Frank Pollmann, "Crossing a topological phase transition with a quantum computer", arXiv:1910.05351.

[100] Ciarán Ryan-Anderson, "Quantum Algorithms, Architecture, and Error Correction", arXiv:1812.04735.

[101] Ruslan Shaydulin, Hayato Ushijima-Mwesigwa, Ilya Safro, Susan Mniszewski, and Yuri Alexeev, "Community Detection Across Emerging Quantum Architectures", arXiv:1810.07765.

[102] Yuxuan Du, Tongliang Liu, and Dacheng Tao, "Bayesian Quantum Circuit", arXiv:1805.11089.

[103] Keisuke Fujii, "Quantum speedup in stoquastic adiabatic quantum computation", arXiv:1803.09954.

[104] Xi Chen, Bin Cheng, Zhaokai Li, Xinfang Nie, Nengkun Yu, Man-Hong Yung, and Xinhua Peng, "Experimental Cryptographic Verification for Near-Term Quantum Cloud Computing", arXiv:1808.07375.

[105] Anton Robert, Panagiotis Kl. Barkoutsos, Stefan Woerner, and Ivano Tavernelli, "Resource-Efficient Quantum Algorithm for Protein Folding", arXiv:1908.02163.

[106] Yosi Atia, Yonathan Oren, and Nadav Katz, "Robust Diabatic Grover Search by Landau-Zener-Stückelberg Oscillations", Entropy 21 10, 937 (2019).

[107] Ajinkya Borle and Josh McCarter, "On Post-Processing the Results of Quantum Optimizers", arXiv:1905.13107.

[108] Kyle Cormier, Riccardo Di Sipio, and Peter Wittek, "Unfolding measurement distributions via quantum annealing", Journal of High Energy Physics 2019 11, 128 (2019).

[109] Adam Holmes, Yongshan Ding, Ali Javadi-Abhari, Diana Franklin, Margaret Martonosi, and Frederic T. Chong, "Resource Optimized Quantum Architectures for Surface Code Implementations of Magic-State Distillation", arXiv:1904.11528.

[110] Yuta Matsuzawa and Yuki Kurashige, "A Jastrow-type decomposition in quantum chemistry for low-depth quantum circuits", arXiv:1909.12410.

[111] Narayanan Rengaswamy, Robert Calderbank, Swanand Kadhe, and Henry D. Pfister, "Logical Clifford Synthesis for Stabilizer Codes", arXiv:1907.00310.

[112] Toshinari Itoko, Rudy Raymond, Takashi Imamichi, and Atsushi Matsuo, "Optimization of Quantum Circuit Mapping using Gate Transformation and Commutation", arXiv:1907.02686.

[113] Naeimeh Mohseni, Marek Narozniak, Alexey N. Pyrkov, Valentin Ivannikov, Jonathan P. Dowling, and Tim Byrnes, "Error suppression in adiabatic quantum computing with qubit ensembles", arXiv:1909.09947.

[114] Zhenyu Cai and Simon C. Benjamin, "Constructing Smaller Pauli Twirling Sets for Arbitrary Error Channels", Scientific Reports 9, 11281 (2019).

[115] Travis L. Scholten, Yi-Kai Liu, Kevin Young, and Robin Blume-Kohout, "Classifying single-qubit noise using machine learning", arXiv:1908.11762.

[116] Abdullah Ash Saki, Mahabubul Alam, and Swaroop Ghosh, "Study of Decoherence in Quantum Computers: A Circuit-Design Perspective", arXiv:1904.04323.

[117] Sebastien Piat, Nairi Usher, Simone Severini, Mark Herbster, Tommaso Mansi, and Peter Mountney, "Image classification with quantum pre-training and auto-encoders", International Journal of Quantum Information 16 8, 1840009-332 (2018).

[118] Zhi-Yuan Li, Hai-Feng Yu, Xin-Sheng Tan, Shi-Ping Zhao, and Yang Yu, "Manipulation of superconducting qubit with direct digital synthesis", Chinese Physics B 28 9, 098505 (2019).

[119] Mark B. Ritter, "Near-term Quantum Algorithms for Quantum Many-body Systems", Journal of Physics Conference Series 1290 1, 012003 (2019).

[120] Karthik Chinni, Pablo Poggi, and Ivan Deutsch, "Reliable Analog Quantum Simulation and Quantum Complexity", APS March Meeting Abstracts 2019, C42.011 (2019).

[121] Stefano Gandolfi, "Cloud Quantum Computing Tackles Simple Nucleus", Physics Online Journal 11, 51 (2018).

[122] Daniel Vert, Renaud Sirdey, and Stéphane Louise, "Revisiting old combinatorial beasts in the quantum age: quantum annealing versus maximal matching", arXiv:1910.05129.

[123] Anurag Mishra and Alireza Shabani, "High-Quality Protein Force Fields with Noisy Quantum Processors", arXiv:1907.07128.

[124] Sumsam Ullah Khan, Ahsan Javed Awan, and Gemma Vall-Llosera, "K-Means Clustering on Noisy Intermediate Scale Quantum Computers", arXiv:1909.12183.

[125] Matthew Otten and Stephen K. Gray, "Accounting for errors in quantum algorithms via individual error reduction", npj Quantum Information 5, 11 (2019).

[126] Bálint Koczor and Simon C. Benjamin, "Quantum natural gradient generalised to non-unitary circuits", arXiv:1912.08660.

[127] Michael Broughton, Guillaume Verdon, Trevor McCourt, Antonio J. Martinez, Jae Hyeon Yoo, Sergei V. Isakov, Philip Massey, Murphy Yuezhen Niu, Ramin Halavati, Evan Peters, Martin Leib, Andrea Skolik, Michael Streif, David Von Dollen, Jarrod R. McClean, Sergio Boixo, Dave Bacon, Alan K. Ho, Hartmut Neven, and Masoud Mohseni, "TensorFlow Quantum: A Software Framework for Quantum Machine Learning", arXiv:2003.02989.

[128] Deanna M. Abrams, Nicolas Didier, Blake R. Johnson, Marcus P. da Silva, and Colm A. Ryan, "Implementation of the XY interaction family with calibration of a single pulse", arXiv:1912.04424.

[129] P. A. M. Casares and M. A. Martin-Delgado, "A quantum interior-point predictor-corrector algorithm for linear programming", Journal of Physics A Mathematical General 53 44, 445305 (2020).

[130] Nai-Hui Chia, András Gilyén, Tongyang Li, Han-Hsuan Lin, Ewin Tang, and Chunhao Wang, "Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning", arXiv:1910.06151.

[131] Kanav Setia, Richard Chen, Julia E. Rice, Antonio Mezzacapo, Marco Pistoia, and James Whitfield, "Reducing qubit requirements for quantum simulation using molecular point group symmetries", arXiv:1910.14644.

[132] Barnaby van Straaten and Bálint Koczor, "Measurement cost of metric-aware variational quantum algorithms", arXiv:2005.05172.

[133] D. Chivilikhin, A. Samarin, V. Ulyantsev, I. Iorsh, A. R. Oganov, and O. Kyriienko, "MoG-VQE: Multiobjective genetic variational quantum eigensolver", arXiv:2007.04424.

[134] Suguru Endo, Zhenyu Cai, Simon C. Benjamin, and Xiao Yuan, "Hybrid quantum-classical algorithms and quantum error mitigation", arXiv:2011.01382.

[135] Prakash Murali, David C. McKay, Margaret Martonosi, and Ali Javadi-Abhari, "Software Mitigation of Crosstalk on Noisy Intermediate-Scale Quantum Computers", arXiv:2001.02826.

[136] Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Dacheng Tao, and Nana Liu, "Quantum noise protects quantum classifiers against adversaries", arXiv:2003.09416.

[137] Kyle Poland, Kerstin Beer, and Tobias J. Osborne, "No Free Lunch for Quantum Machine Learning", arXiv:2003.14103.

[138] Patrick Huembeli and Alexandre Dauphin, "Characterizing the loss landscape of variational quantum circuits", arXiv:2008.02785.

[139] Johannes Jakob Meyer, Johannes Borregaard, and Jens Eisert, "A variational toolbox for quantum multi-parameter estimation", arXiv:2006.06303.

[140] Alba Cervera-Lierta, Jakob S. Kottmann, and Alán Aspuru-Guzik, "The Meta-Variational Quantum Eigensolver (Meta-VQE): Learning energy profiles of parameterized Hamiltonians for quantum simulation", arXiv:2009.13545.

[141] Kouhei Nakaji and Naoki Yamamoto, "Expressibility of the alternating layered ansatz for quantum computation", arXiv:2005.12537.

[142] Xin Wang, Zhixin Song, and Youle Wang, "Variational Quantum Singular Value Decomposition", arXiv:2006.02336.

[143] Ian MacCormack, Mao Tian Tan, Jonah Kudler-Flam, and Shinsei Ryu, "Operator and entanglement growth in non-thermalizing systems: many-body localization and the random singlet phase", arXiv:2001.08222.

[144] Bálint Koczor, "Exponential Error Suppression for Near-Term Quantum Devices", arXiv:2011.05942.

[145] Michel Fabrice Serret, Bertrand Marchand, and Thomas Ayral, "Solving optimization problems with Rydberg analog quantum computers: Realistic requirements for quantum advantage using noisy simulation and classical benchmarks", arXiv:2006.11190.

[146] Mark Hodson, Brendan Ruck, Hugh Ong, David Garvin, and Stefan Dulman, "Portfolio rebalancing experiments using the Quantum Alternating Operator Ansatz", arXiv:1911.05296.

[147] Hyeokjea Kwon and Joonwoo Bae, "A hybrid quantum-classical approach to mitigating measurement errors", arXiv:2003.12314.

[148] Srinivasan Arunachalam and Reevu Maity, "Quantum Boosting", arXiv:2002.05056.

[149] Shahnawaz Ahmed, Carlos Sánchez Muñoz, Franco Nori, and Anton Frisk Kockum, "Quantum State Tomography with Conditional Generative Adversarial Networks", arXiv:2008.03240.

[150] András Gilyén, Zhao Song, and Ewin Tang, "An improved quantum-inspired algorithm for linear regression", arXiv:2009.07268.

[151] Rui Chao, Dawei Ding, Andras Gilyen, Cupjin Huang, and Mario Szegedy, "Finding Angles for Quantum Signal Processing with Machine Precision", arXiv:2003.02831.

[152] Young-Hyun Oh, Hamed Mohammadbagherpoor, Patrick Dreher, Anand Singh, Xianqing Yu, and Andy J. Rindos, "Solving Multi-Coloring Combinatorial Optimization Problems Using Hybrid Quantum Algorithms", arXiv:1911.00595.

[153] Matteo G. Pozzi, Steven J. Herbert, Akash Sengupta, and Robert D. Mullins, "Using Reinforcement Learning to Perform Qubit Routing in Quantum Compilers", arXiv:2007.15957.

[154] Hamed Mohammadbagherpoor, Young-Hyun Oh, Patrick Dreher, Anand Singh, Xianqing Yu, and Andy J. Rindos, "An Improved Implementation Approach for Quantum Phase Estimation on Quantum Computers", arXiv:1910.11696.

[155] Ranyiliu Chen, Zhixin Song, Xuanqiang Zhao, and Xin Wang, "Variational Quantum Algorithms for Trace Distance and Fidelity Estimation", arXiv:2012.05768.

[156] Martin Kliesch and Ingo Roth, "Theory of quantum system certification -- a tutorial", arXiv:2010.05925.

[157] Samudra Dasgupta and Travis S. Humble, "Characterizing the Stability of NISQ Devices", arXiv:2008.09612.

[158] Man-Hong Yung and Bin Cheng, "Anti-Forging Quantum Data: Cryptographic Verification of Quantum Cloud Computing", arXiv:2005.01510.

[159] Danijela Marković and Julie Grollier, "Quantum neuromorphic computing", Applied Physics Letters 117 15, 150501 (2020).

[160] Shahnawaz Ahmed, Carlos Sánchez Muñoz, Franco Nori, and Anton Frisk Kockum, "Classification and reconstruction of optical quantum states with deep neural networks", arXiv:2012.02185.

[161] Kenji Kubo, Yuya O. Nakagawa, Suguru Endo, and Shota Nagayama, "Variational quantum simulations of stochastic differential equations", arXiv:2012.04429.

[162] Dan-Bo Zhang, Hongxi Xing, Hui Yan, Enke Wang, and Shi-Liang Zhu, "Selected topics of quantum computing for nuclear physics", arXiv:2011.01431.

[163] Jelmer J. Renema, "Simulability of Imperfect Gaussian and Superposition Boson Sampling", arXiv:1911.10112.

[164] Inés de Vega, "The quantum dynamical map of the spin boson model", arXiv:2001.04236.

[165] Evandro Chagas Ribeiro da Rosa and Bruno G. Taketani, "QSystem: bitwise representation for quantum circuit simulations", arXiv:2004.03560.

[166] Narayanan Rengaswamy, "Classical Coding Approaches to Quantum Applications", arXiv:2004.06834.

[167] Leigh S. Martin, "Quantum feedback for measurement and control", arXiv:2004.09766.

[168] Runyao Duan, "Quantum Adiabatic Theorem Revisited", arXiv:2003.03063.

[169] Evandro Chagas Ribeiro da Rosa and Rafael de Santiago, "Classical and Quantum Data Interaction in Programming Languages: A Runtime Architecture", arXiv:2006.00131.

[170] Olivia Di Matteo, Anna McCoy, Peter Gysbers, Takayuki Miyagi, R. M. Woloshyn, and Petr Navrátil, "Improving Hamiltonian encodings with the Gray code", arXiv:2008.05012.

[171] William Cappelletti, Rebecca Erbanni, and Joaquín Keller, "Polyadic Quantum Classifier", arXiv:2007.14044.

[172] Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, and Xiaodi Wu, "Quantum Wasserstein Generative Adversarial Networks", arXiv:1911.00111.

[173] Casper Gyurik, Chris Cade, and Vedran Dunjko, "Towards quantum advantage via topological data analysis", arXiv:2005.02607.

[174] H. W. L. Naus, "NISQ computing for decision making under uncertainty", arXiv:1911.06167.

[175] Pedro Rivero, Ian C. Cloët, and Zack Sullivan, "An optimal quantum sampling regression algorithm for variational eigensolving in the low qubit number regime", arXiv:2012.02338.

[176] Huo Chen and Daniel A. Lidar, "HOQST: Hamiltonian Open Quantum System Toolkit", arXiv:2011.14046.

[177] Arit Kumar Bishwas, Ashish Mani, and Vasile Palade, "Parts of Speech Tagging in NLP: Runtime Optimization with Quantum Formulation and ZX Calculus", arXiv:2007.10328.

[178] Haozhen Situ and Zhimin He, "Machine learning distributions of quantum ansatz with hierarchical structure", International Journal of Modern Physics B 34 20, 2050196-3746 (2020).

[179] Kang Cai, Prabin Parajuli, Guilu Long, Chee Wei Wong, and Lin Tian, "Robust Preparation of Many-body Ground States in Jaynes-Cummings Lattices", arXiv:2007.02218.

[180] Mark Hodson, Brendan Ruck, Hugh Ong, Stefan Dulman, and David Garvin, "Finding the optimal Nash equilibrium in a discrete Rosenthal congestion game using the Quantum Alternating Operator Ansatz", arXiv:2008.09505.

[181] Connor T. Hann, Gideon Lee, S. M. Girvin, and Liang Jiang, "The resilience of quantum random access memory to generic noise", arXiv:2012.05340.

[182] Daniel Malz and Adam Smith, "Topological two-dimensional Floquet lattice on a single superconducting qubit", arXiv:2012.01459.

[183] Minsung Kim, Davide Venturelli, and Kyle Jamieson, "Towards Hybrid Classical-Quantum Computation Structures in Wirelessly-Networked Systems", arXiv:2010.00682.

[184] Rafael I. Nepomechie, "Bethe ansatz on a quantum computer?", arXiv:2010.01609.

[185] Chenyi Zhang, Jiaqi Leng, and Tongyang Li, "Quantum Algorithms for Escaping from Saddle Points", arXiv:2007.10253.

[186] Nivedita Dey, Mrityunjay Ghosh, Subhra Samir kundu, and Amlan Chakrabarti, "QDLC -- The Quantum Development Life Cycle", arXiv:2010.08053.

[187] David W. Kribs, Ningping Cao, Chi-Kwong Li, Yiu-Tung Poon, Bei Zeng, and Mike Nelson, "Higher Rank Matricial Ranges and Hybrid Quantum Error Correction", arXiv:1911.12744.

[188] Qihao Guo, Yuan-Yuan Zhao, Markus Grassl, Xinfang Nie, Guo-Yong Xiang, Tao Xin, Zhang-Qi Yin, and Bei Zeng, "Testing a Quantum Error-Correcting Code on Various Platforms", arXiv:2001.07998.

[189] Rodrigo S. Sousa, Priscila G. M. dos Santos, Tiago M. L. Veras, Wilson R. de Oliveira, and Adenilton J. da Silva, "Parametric Probabilistic Quantum Memory", arXiv:2001.04798.

[190] Mahmoud Mahdian and H. Davoodi Yeganeh, "Incoherent quantum algorithm dynamics of an open system with near-term devices", Quantum Information Processing 19 9, 285 (2020).

[191] Shaopeng Zhu, Shih-Han Hung, Shouvanik Chakrabarti, and Xiaodi Wu, "On the Principles of Differentiable Quantum Programming Languages", arXiv:2004.01122.

[192] Tanvi P. Gujarati, Tyler Takeshita, Andreas Hintennach, and Eunseok Lee, "A Heuristic Quantum-Classical Algorithm for Modeling Substitutionally Disordered Binary Crystalline Materials", arXiv:2004.00957.

[193] Thomas Gabor, Leo Sünkel, Fabian Ritz, Thomy Phan, Lenz Belzner, Christoph Roch, Sebastian Feld, and Claudia Linnhoff-Popien, "The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline", arXiv:2004.14035.

[194] Thomas Häner and Mathias Soeken, "Lowering the T-depth of Quantum Circuits By Reducing the Multiplicative Depth Of Logic Networks", arXiv:2006.03845.

[195] Adam Winick, Joel J. Wallman, and Joseph Emerson, "Simulating and mitigating crosstalk", arXiv:2006.09596.

[196] Omer Sakarya, Marek Winczewski, Adam Rutkowski, and Karol Horodecki, "Memory Cost of an Anti-malware Quantum Network Design", arXiv:1912.07548.

[197] H. W. L. Naus and H. Polinder, "Bell inequality violation on small NISQ computers", arXiv:2006.13794.

[198] Jasvith Raj Basani and Aranya B Bhattacherjee, "Continuous-Variable Deep Quantum Neural Networks for Flexible Learning of Structured Classical Information", arXiv:2006.10927.

[199] Atchade Parfait Adelomou, Elisabet Golobardes Ribe, and Xavier Vilasis Cardona, "Using the Parameterized Quantum Circuit combined with Variational-Quantum-Eigensolver (VQE) to create an Intelligent social workers' schedule problem solver", arXiv:2010.05863.

[200] Kelvin Loh, "Fairness evaluation during the conceptual design of heat grids with quantum annealers", arXiv:1910.09929.

[201] Minsung Kim, Davide Venturelli, and Kyle Jamieson, "Leveraging Quantum Annealing for Large MIMO Processing in Centralized Radio Access Networks", arXiv:2001.04014.

[202] Jarosław Adam Miszczak, "Variational certification of quantum devices", arXiv:2011.01879.

[203] Sam McArdle, "Learning from physics experiments, with quantum computers: Applications in muon spectroscopy", arXiv:2012.06602.

[204] Tongyang Li, Chunhao Wang, Shouvanik Chakrabarti, and Xiaodi Wu, "Sublinear classical and quantum algorithms for general matrix games", arXiv:2012.06519.

[205] Ian MacCormack, Conor Delaney, Alexey Galda, Nidhi Aggarwal, and Prineha Narang, "Branching Quantum Convolutional Neural Networks", arXiv:2012.14439.

[206] Arpita Sanyal, Amit Saha, Banani Saha, and Amlan Chakrabarti, "Circuit Design Of Clique Problem And Its Implementation On NISQ Using Combinatorial Approach Of Classical-Quantum Hybrid Model", arXiv:2004.10596.

[207] Bhupesh Bishnoi, "Quantum Computation", arXiv:2006.02799.

[208] Dominik Hangleiter, "Sampling and the complexity of nature", arXiv:2012.07905.

[209] Stefan H. Sack and Maksym Serbyn, "Quantum annealing initialization of the quantum approximate optimization algorithm", arXiv:2101.05742.

The above citations are from SAO/NASA ADS (last updated successfully 2021-01-21 04:05:59). The list may be incomplete as not all publishers provide suitable and complete citation data.

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