Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash 99 tok/s
Gemini 2.5 Pro 48 tok/s Pro
GPT-5 Medium 36 tok/s
GPT-5 High 40 tok/s Pro
GPT-4o 99 tok/s
GPT OSS 120B 461 tok/s Pro
Kimi K2 191 tok/s Pro
2000 character limit reached

Quantum Digital Twins for Uncertainty Quantification (2410.23311v1)

Published 29 Oct 2024 in quant-ph

Abstract: Modern supercomputers can handle resource-intensive computational and data-driven problems in various industries and academic fields. These supercomputers are primarily made up of traditional classical resources comprising CPUs and GPUs. Integrating quantum processing units with supercomputers offers the potential to accelerate and manage computationally intensive subroutines currently handled by CPUs or GPUs. However, the presence of noise in quantum processing units limits their ability to provide a clear quantum advantage over conventional classical resources. Hence, we develop and construct "quantum digital twins," virtual versions of quantum processing units. To demonstrate the potential benefit of quantum digital twins, we create and deploy hybrid quantum ensembles on five quantum digital twins that emulate parallel quantum computers since hybrid quantum ensembles are suitable for distributed computing. Our study demonstrates that quantum digital twins assist in analyzing the actual quantum device noise on real-world use cases and emulate parallel quantum processing units for distributed computational tasks to obtain quantum advantage as early as possible.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (20)
  1. Y. Kawazura and S. S. Kimura, “Inertial range of magnetorotational turbulence,” Science Advances, vol. 10, no. 35, 2024. [Online]. Available: https://www.science.org/doi/abs/10.1126/sciadv.adp4965
  2. V. Eyring, W. D. Collins, P. Gentine, E. A. Barnes, M. Barreiro, T. Beucler, M. Bocquet, C. S. Bretherton, H. M. Christensen, K. Dagon, D. J. Gagne, D. Hall, D. Hammerling, S. Hoyer, F. Iglesias-Suarez, I. Lopez-Gomez, M. C. McGraw, G. A. Meehl, M. J. Molina, C. Monteleoni, J. Mueller, M. S. Pritchard, D. Rolnick, J. Runge, P. Stier, O. Watt-Meyer, K. Weigel, R. Yu, and L. Zanna, “Pushing the frontiers in climate modelling and analysis with machine learning,” Nature Climate Change, 2024. [Online]. Available: https://doi.org/10.1038/s41558-024-02095-y
  3. A. Aspuru-Guzik, A. D. Dutoi, P. J. Love, and M. Head-Gordon, “Simulated quantum computation of molecular energies,” Science, vol. 309, no. 5741, pp. 1704–1707, 2005. [Online]. Available: https://www.science.org/doi/abs/10.1126/science.1113479
  4. A. Miessen, P. J. Ollitrault, F. Tacchino, and I. Tavernelli, “Quantum algorithms for quantum dynamics,” Nature Computational Science, vol. 3, no. 1, pp. 25–37, Jan 2023. [Online]. Available: https://doi.org/10.1038/s43588-022-00374-2
  5. J. Liu, M. Liu, J.-P. Liu, Z. Ye, Y. Wang, Y. Alexeev, J. Eisert, and L. Jiang, “Towards provably efficient quantum algorithms for large-scale machine-learning models,” Nature Communications, vol. 15, no. 1, p. 434, 2024. [Online]. Available: https://doi.org/10.1038/s41467-023-43957-x
  6. Y. Alexeev, M. Amsler, and et. al., “Quantum-centric supercomputing for materials science: A perspective on challenges and future directions,” Future Generation Computer Systems, vol. 160, pp. 666–710, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167739X24002012
  7. J. Preskill, “Quantum Computing in the NISQ era and beyond,” Quantum, vol. 2, p. 79, 2018. [Online]. Available: https://doi.org/10.22331/q-2018-08-06-79
  8. M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, and P. J. Coles, “Variational quantum algorithms,” Nature Reviews Physics, vol. 3, pp. 625–644, 2021. [Online]. Available: https://doi.org/10.1038/s42254-021-00348-9
  9. S. V. Isakov, D. Kafri, O. Martin, C. V. Heidweiller, W. Mruczkiewicz, M. P. Harrigan, N. C. Rubin, R. Thomson, M. Broughton, K. Kissell, E. Peters, E. Gustafson, A. C. Y. Li, H. Lamm, G. Perdue, A. K. Ho, D. Strain, and S. Boixo, “Simulations of quantum circuits with approximate noise using qsim and cirq,” arXiv, 2021. [Online]. Available: https://arxiv.org/abs/2111.02396
  10. (last accessed in 2024) Quantum Digital Twins. [Online]. Available: https://github.com/sozoluffy/Quantum-Digital-Twins/tree/main
  11. B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles,” in Advances in Neural Information Processing Systems, vol. 30, 2017. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf
  12. M. Pandey, M. Fernandez, F. Gentile, O. Isayev, A. Tropsha, A. C. Stern, and A. Cherkasov, “The transformational role of GPU computing and deep learning in drug discovery,” Nature Machine Intelligence, vol. 4, no. 3, pp. 211–221, 2022. [Online]. Available: https://doi.org/10.1038/s42256-022-00463-x
  13. J. Willard, X. Jia, S. Xu, M. Steinbach, and V. Kumar, “Integrating scientific knowledge with machine learning for engineering and environmental systems,” arXiv, 2021. [Online]. Available: https://arxiv.org/abs/2003.04919
  14. Y. Gal, P. Koumoutsakos, F. Lanusse, G. Louppe, and C. Papadimitriou, “Bayesian uncertainty quantification for machine-learned models in physics,” Nature Reviews Physics, vol. 4, no. 9, pp. 573–577, 2022. [Online]. Available: https://doi.org/10.1038/s42254-022-00498-4
  15. C. Rudin, “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,” arXiv, 2018. [Online]. Available: https://arxiv.org/abs/1811.10154
  16. W. Li, Z. Yin, X. Li, D. Ma, S. Yi, Z. Zhang, C. Zou, K. Bu, M. Dai, J. Yue, Y. Chen, X. Zhang, and S. Zhang, “A hybrid quantum computing pipeline for real world drug discovery,” Scientific Reports, vol. 14, no. 1, p. 16942, Jul 2024. [Online]. Available: https://doi.org/10.1038/s41598-024-67897-8
  17. S. Otgonbaatar and M. Datcu, “Classification of remote sensing images with parameterized quantum gates,” IEEE Geoscience and Remote Sensing Letters, pp. 1–5, 2021. [Online]. Available: https://doi.org/10.1109/LGRS.2021.3108014
  18. (last accessed in 2024) IBM Quantum Experience. [Online]. Available: https://quantum-computing.ibm.com/
  19. S. Otgonbaatar and D. Kranzlmüller, “Exploiting the quantum advantage for satellite image processing: Review and assessment,” IEEE Transactions on Quantum Engineering, vol. 5, pp. 1–9, 2024. [Online]. Available: https://ieeexplore.ieee.org/document/10339907
  20. L. Schmitt, C. Piveteau, and D. Sutter, “Cutting circuits with multiple two-qubit unitaries,” 2024. [Online]. Available: https://arxiv.org/abs/2312.11638
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets