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Quantum computers progress toward outperforming classical supercomputers, but quantum errors remain their primary obstacle.
Charles H. Bennett, Gilles Brassard, Sandu Popescu, Benjamin Schumacher, John A. Smolin, and William K. Wootters, “Purification of noisy entanglement and faithful teleportation via noisy channels,” Physical Review Letters 76
1996
Earlier work this paper cites.
Aram W. Harrow and Richard A. Low, “Random quantum circuits are approximate 2-designs,” Communications in Mathematical Physics 291
2009
Earlier work this paper cites.
Michael R. Geller and Zhongyuan Zhou, “Efficient error models for fault-tolerant architectures and the pauli twirling approximation,” Physical Review A 88
2013
Earlier work this paper cites.
2015
Earlier work this paper cites.
Joel J. Wallman and Joseph Emerson, “Noise tailoring for scalable quantum computation via randomized compiling,” Physical Review A 94
2016
Earlier work this paper cites.
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd, “Quantum machine learning,” Nature 549
2017
Earlier work this paper cites.
Earl T. Campbell, Barbara M. Terhal, and Christophe Vuillot, “Roads towards fault-tolerant universal quantum computation,” Nature 549
2017
Earlier work this paper cites.
Kristan Temme, Sergey Bravyi, and Jay M. Gambetta, “Error mitigation for short-depth quantum circuits,” Physical Review Letters 119
2017
Earlier work this paper cites.
Ying Li and Simon C. Benjamin, “Efficient variational quantum simulator incorporating active error minimization,” Physical Review X 7
2017
Earlier work this paper cites.
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
2017
Earlier work this paper cites.
Alex Kendall and Yarin Gal, “What uncertainties do we need in bayesian deep learning for computer vision?” in Proceedings of the 31st International Conference on Neural Information Processing Systems (2017) p. 5580–5590
2017
Earlier work this paper cites.
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec, “Graph convolutional neural networks for web-scale recommender systems,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2018) p. 974–983
2018
Earlier work this paper cites.
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
2019
Earlier work this paper cites.
Ekagra Ranjan, Soumya Sanyal, and Partha Pratim Talukdar, “Asap: Adaptive structure aware pooling for learning hierarchical graph representations,” in AAAI Conference on Artificial Intelligence (2019)
2019
Earlier work this paper cites.
Suguru Endo, Qi Zhao, Ying Li, Simon Benjamin, and Xiao Yuan, “Mitigating algorithmic errors in a hamiltonian simulation,” Physical Review A 99
2019
Earlier work this paper cites.
Changjun Kim, Kyungdeock Daniel Park, and June-Koo Rhee, “Quantum error mitigation with artificial neural network,” IEEE Access 8
2020
Earlier work this paper cites.
Jarrod R. McClean, Zhang Jiang, Nicholas C. Rubin, Ryan Babbush, and Hartmut Neven, “Decoding quantum errors with subspace expansions,” Nature Communications 11
2020
Cited alongside, same era.
Piotr Czarnik, Andrew Arrasmith, Patrick J. Coles, and Lukasz Cincio, “Error mitigation with clifford quantum-circuit data,” Quantum 5
2021
Cited alongside, same era.
Tirthak Patel and Devesh Tiwari, “Qraft: Reverse your quantum circuit and know the correct program output,” in Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (2021) p. 443–455
2021
Cited alongside, same era.
Armands Strikis, Dayue Qin, Yanzhu Chen, Simon C. Benjamin, and Ying Li, “Learning-based quantum error mitigation,” PRX Quantum 2
2021
Cited alongside, same era.
Ewout van den Berg, Zlatko K. Minev, and Kristan Temme, “Model-free readout-error mitigation for quantum expectation values,” Physical Review A 105
2022
Later among the works it cites.
Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, and Pascal Friederich, “Graph neural networks for materials science and chemistry,” Communications Materials 3
2022
Later among the works it cites.
Pedro Rivero, Friederike Metz, Areeq Hasan, Agata M. Brańczyk, and Caleb Johnson, “Zero noise extrapolation prototype,” https://github.com/qiskit-community/prototype-zne (2022)
2022
Later among the works it cites.
Zlatko Minev, “A tutorial on tailoring quantum noise - twirling 101,” (2022)
2022
Later among the works it cites.
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2021
Cited alongside, same era.
Akel Hashim, Ravi K. Naik, Alexis Morvan, Jean-Loup Ville, Bradley Mitchell, John Mark Kreikebaum, Marc Davis, Ethan Smith, Costin Iancu, Kevin P. O’Brien, Ian Hincks, Joel J. Wallman, Joseph Emerson, and Irfan Siddiqi, “Randomized compiling for scalable quantum computing on a noisy superconducting quantum processor,” Physical Review X 11
2021
Cited alongside, same era.
Angus Lowe, Max Hunter Gordon, Piotr Czarnik, Andrew Arrasmith, Patrick J. Coles, and Lukasz Cincio, “Unified approach to data-driven quantum error mitigation,” Physical Review Research 3
2021
Cited alongside, same era.
Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjing Wang, and Yu Sun, “Masked label prediction: Unified message passing model for semi-supervised classification,” in Proceedings of the 13th International Joint Conference on Artificial Intelligence, IJCAI-21 (2021) pp. 1548–1554
2021
Cited alongside, same era.
Haoran Liao, Ian Convy, William J. Huggins, and K. Birgitta Whaley, “Robust in practice: Adversarial attacks on quantum machine learning,” Physical Review A 103
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Andrew J. Daley, Immanuel Bloch, Christian Kokail, Stuart Flannigan, Natalie Pearson, Matthias Troyer, and Peter Zoller, “Practical quantum advantage in quantum simulation,” Nature 607
2022
Cited alongside, same era.
Sergey Bravyi, Oliver Dial, Jay M. Gambetta, Darío Gil, and Zaira Nazario, “The future of quantum computing with superconducting qubits,” Journal of Applied Physics 132
2022
Cited alongside, same era.
2023
Closest in time.
Ewout van den Berg, Zlatko K. Minev, Abhinav Kandala, and Kristan Temme, “Probabilistic error cancellation with sparse pauli-lindblad models on noisy quantum processors,” Nature Physics (2023)
2023
Closest in time.
Kento Tsubouchi, Takahiro Sagawa, and Nobuyuki Yoshioka, “Universal cost bound of quantum error mitigation based on quantum estimation theory,” Physical Review Letters 131
2023
Closest in time.
Ryuji Takagi, Hiroyasu Tajima, and Mile Gu, “Universal sampling lower bounds for quantum error mitigation,” Physical Review Letters 131
2023
Closest in time.
2023
Closest in time.
Nic Ezzell, Bibek Pokharel, Lina Tewala, Gregory Quiroz, and Daniel A. Lidar, “Dynamical decoupling for superconducting qubits: A performance survey,” Physical Review Applied 20
2023
Closest in time.
Bibek Pokharel and Daniel A. Lidar, “Demonstration of algorithmic quantum speedup,” Physical Review Letters 130
2023
Closest in time.
2023
Closest in time.
Yihui Quek, Daniel Stilck França, Sumeet Khatri, Johannes Jakob Meyer, and Jens Eisert, “Exponentially tighter bounds on limitations of quantum error mitigation,” Nature Physics 20
2024
Closest in time.
Alireza Seif, Haoran Liao, Vinay Tripathi, Kevin Krsulich, Moein Malekakhlagh, Mirko Amico, Petar Jurcevic, and Ali Javadi-Abhari, “Suppressing correlated noise in quantum computers via context-aware compiling,” in 2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA) (2024) pp. 310–324
2024
Closest in time.
Iskandar Sitdikov, Zlatko K. Minev, and Haoran Liao, “Machine learning for practical quantum error mitigation,” Zenodo (2024), https://doi.org/10.5281/zenodo.13769804
2024
Closest in time.