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Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

Journal Article · · TBD
OSTI ID:2282613
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  1. Argonne, PHY
  2. Unlisted, DE
  3. Rio de Janeiro, IMPA; Rio de Janeiro, CBPF
  4. CINECA
  5. Arizona State U.
  6. LBL, Berkeley
  7. Donostia Intl. Phys. Ctr., San Sebastian; IKERBASQUE, Bilbao; Basque U., Bilbao
  8. Yonsei U.
  9. Chicago U.
  10. IBM Watson Res. Ctr.
  11. Cambridge U.; Cambridge U., DAMTP
  12. CERN
  13. Virginia Tech.
  14. Los Alamos
  15. Rio de Janeiro, IMPA
  16. Osaka U.
  17. U. Chicago (main); Argonne, PHY
  18. Chicago U.; Argonne, PHY
  19. Fraunhofer Inst., Kaiserslautern
  20. Unlisted, US
  21. Illinois U., Urbana
  22. Michigan U.
  23. Harbin Inst. Tech.
  24. Oak Ridge
  25. RIKEN AICS, Kobe
  26. Toronto U.; Toronto U., Scarborough Coll.
  27. Brookhaven; Rutgers U., Piscataway
  28. IBM, San Jose
  29. LBNL, Berkeley
  30. IBM, Cambridge
  31. Unlisted, UK
  32. Taejon, Elect. Telecomm. Res. Inst.
  33. Keio U.; RIKEN AICS, Kobe
  34. Fermilab
  35. Poznan Tech. U.
  36. IBM, Zurich
  37. LLNL, Livermore
  38. PNL, Richland; Washington U., Seattle
  39. Brookhaven
  40. Basque U., San Sebastian; IKERBASQUE, Bilbao; Donostia Intl. Phys. Ctr., San Sebastian
  41. Unlisted
  42. Princeton U.
  43. IAS, Julich
  44. U. Chicago (main)
  45. UC, Berkeley; LBL, Berkeley
  46. Brookhaven; Cal State, L.A.
  47. Toshiba, Kawasaki
  48. Wisconsin U., Madison
  49. Unlisted, IE
  50. Unlisted, FI
  51. KISTI, Daejeon
  52. Utrecht U.
  53. Artep Inc.
  54. Donostia Intl. Phys. Ctr., San Sebastian; IKERBASQUE, Bilbao
  55. USRA, Huntsville
  56. Tokyo U.
  57. Sandia
  58. Maryland U.
  59. RIKEN AICS, Kobe; Nishina Ctr., RIKEN
+ Show Author Affiliations

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP)
DOE Contract Number:
AC02-07CH11359
OSTI ID:
2282613
Report Number(s):
FERMILAB-PUB-24-0001-SQMS; arXiv:2312.09733; oai:inspirehep.net:2737763
Journal Information:
TBD, Journal Name: TBD
Country of Publication:
United States
Language:
English

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