Identifying Materials-Level Sources of Performance Variation in Superconducting Transmon Qubits

  1. Akshay A. Murthy,
  2. Mustafa Bal,
  3. Michael J. Bedzyk,
  4. Hilal Cansizoglu,
  5. Randall K. Chan,
  6. Venkat Chandrasekhar,
  7. Francesco Crisa,
  8. Amlan Datta,
  9. Yanpei Deng,
  10. Celeo D. Matute Diaz,
  11. Vinayak P. Dravid,
  12. David A. Garcia-Wetten,
  13. Sabrina Garattoni,
  14. Sunil Ghimire,
  15. Dominic P. Goronzy,
  16. Sebastian de Graaf,
  17. Sam Haeuser,
  18. Mark C. Hersam,
  19. Dieter Isheim,
  20. Kamal Joshi,
  21. Richard Kim,
  22. Saagar Kolachina,
  23. Cameron J. Kopas,
  24. Matthew J. Kramer,
  25. Ella O. Lachman,
  26. Jaeyel Lee,
  27. Peter G. Lim,
  28. Andrei Lunin,
  29. William Mah,
  30. Jayss Marshall,
  31. Josh Y. Mutus,
  32. Jin-Su Oh,
  33. David Olaya,
  34. David P. Pappas,
  35. Joong-mok Park,
  36. Ruslan Prozorov,
  37. Roberto dos Reis,
  38. David N. Seidman,
  39. Zuhawn Sung,
  40. Makariy Tanatar,
  41. Mitchell J. Walker,
  42. Jigang Wang,
  43. Haotian Wu,
  44. Lin Zhou,
  45. Shaojiang Zhu,
  46. Anna Grassellino,
  47. and Alexander Romanenko
The Superconducting Materials and Systems (SQMS) Center, a DOE National Quantum Information Science Research Center, has conducted a comprehensive and coordinated study using superconducting
transmon qubit chips with known performance metrics to identify the underlying materials-level sources of device-to-device performance variation. Following qubit coherence measurements, these qubits of varying base superconducting metals and substrates have been examined with various nondestructive and invasive material characterization techniques at Northwestern University, Ames National Laboratory, and Fermilab as part of a blind study. We find trends in variations of the depth of the etched substrate trench, the thickness of the surface oxide, and the geometry of the sidewall, which when combined, lead to correlations with the T1 lifetime across different devices. In addition, we provide a list of features that varied from device to device, for which the impact on performance requires further studies. Finally, we identify two low-temperature characterization techniques that may potentially serve as proxy tools for qubit measurements. These insights provide materials-oriented solutions to not only reduce performance variations across neighboring devices, but also to engineer and fabricate devices with optimal geometries to achieve performance metrics beyond the state-of-the-art values.

Disentangling the Impact of Quasiparticles and Two-Level Systems on the Statistics of Superconducting Qubit Lifetime

  1. Shaojiang Zhu,
  2. Xinyuan You,
  3. Ugur Alyanak,
  4. Mustafa Bal,
  5. Francesco Crisa,
  6. Sabrina Garattoni,
  7. Andrei Lunin,
  8. Roman Pilipenko,
  9. Akshay Murthy,
  10. Alexander Romanenko,
  11. and Anna Grassellino
Temporal fluctuations in the superconducting qubit lifetime, T1, bring up additional challenges in building a fault-tolerant quantum computer. While the exact mechanisms remain unclear,
T1 fluctuations are generally attributed to the strong coupling between the qubit and a few near-resonant two-level systems (TLSs) that can exchange energy with an assemble of thermally fluctuating two-level fluctuators (TLFs) at low frequencies. Here, we report T1 measurements on the qubits with different geometrical footprints and surface dielectrics as a function of the temperature. By analyzing the noise spectrum of the qubit depolarization rate, Γ1=1/T1, we can disentangle the impact of TLSs, non-equilibrium quasiparticles (QPs), and equilibrium (thermally excited) QPs on the variance in Γ1. We find that Γ1 variances in the qubit with a small footprint are more susceptible to the QP and TLS fluctuations than those in the large-footprint qubits. Furthermore, the QP-induced variances in all qubits are consistent with the theoretical framework of QP diffusion and fluctuation. We suggest these findings can offer valuable insights for future qubit design and engineering optimization.