Nicholas Sharac

Scaling Alternating-Bias-Assisted Annealing for Precision Transmon Frequency Targeting on Superconducting Quantum Processors

  1. Xiqiao Wang,
  2. Mark Field,
  3. Teng Zhang,
  4. Xian Wu,
  5. Ferhat Aydinoglu,
  6. Joel Howard,
  7. Angela Q. Chen,
  8. Sara Elzeiny,
  9. Robert Smith,
  10. Timothy McSorley,
  11. Nicholas Sharac,
  12. Eyob Sete,
  13. Alysson Gold,
  14. Hilal Cansizoglu,
  15. Greg Stiehl,
  16. Josh Mutus,
  17. Kameshwar Yadavalli,
  18. Andrew Bestwick,
  19. Stefano Poletto,
  20. Raja Katta,
  21. and David P. Pappas
Recent advances in the alternating-bias-assisted annealing (ABAA) technique have successfully mitigated intrinsic Josephson-junction (JJ) fabrication variations. This new technique
enables precision qubit frequency tuning alongside simplicity. However, it is critical to enhance tuning throughput and yield while investigating the factors that drive targeting performance as the technology scales. Here, we characterize ABAA tuning performance within a 150-mm wafer process flow and extend this technique to simultaneous, multi-channel tuning, demonstrating that a wafer-scale JJ resistance tuning precision of σ=0.50±0.05% alongside a component-level yield of ≥98.8% can be achieved. Furthermore, we demonstrate a strong correlation between yield, tuning speed, and junction breakdown voltage, establishing the latter as a vital process control parameter for meeting production goals. Finally, we demonstrate a successful implementation of ABAA tuning on a quad-module quantum processor (Rigetti Cepheus-1-36Q), where we achieve an empirical frequency targeting precision of σ∼30 MHz in both qubit and qubit-qubit detuning frequencies, contributing to high median two-qubit gate fidelities. These results confirm the efficacy and scalability of ABAA for high-precision Hamiltonian targeting, a critical enabler for modular superconducting quantum processor technology.
arxiv pdf

Precision frequency tuning of tunable transmon qubits using alternating-bias assisted annealing

  1. Xiqiao Wang,
  2. Joel Howard,
  3. Eyob A. Sete,
  4. Greg Stiehl,
  5. Cameron Kopas,
  6. Stefano Poletto,
  7. Xian Wu,
  8. Mark Field,
  9. Nicholas Sharac,
  10. Christopher Eckberg,
  11. Hilal Cansizoglu,
  12. Raja Katta,
  13. Josh Mutus,
  14. Andrew Bestwick,
  15. Kameshwar Yadavalli,
  16. and David P. Pappas
Superconducting quantum processors are one of the leading platforms for realizing scalable fault-tolerant quantum computation (FTQC). The recent demonstration of post-fabrication tuning
of Josephson junctions using alternating-bias assisted annealing (ABAA) technique and a reduction in junction loss after ABAA illuminates a promising path towards precision tuning of qubit frequency while maintaining high coherence. Here, we demonstrate precision tuning of the maximum |0⟩→|1⟩ transition frequency (fmax01) of tunable transmon qubits by performing ABAA at room temperature using commercially available test equipment. We characterize the impact of junction relaxation and aging on resistance spread after tuning, and demonstrate a frequency equivalent tuning precision of 7.7 MHz (0.17%) based on targeted resistance tuning on hundreds of qubits, with a resistance tuning range up to 18.5%. Cryogenic measurements on tuned and untuned qubits show evidence of improved coherence after ABAA with no significant impact on tunability. Despite a small global offset, we show an empirical fmax01 tuning precision of 18.4 MHz by tuning a set of multi-qubit processors targeting their designed Hamiltonians. We experimentally characterize high-fidelity parametric resonance iSWAP gates on two ABAA-tuned 9-qubit processors with fidelity as high as 99.51±0.20%. On the best-performing device, we measured across the device a median fidelity of 99.22% and an average fidelity of 99.13±0.12%. Yield modeling analysis predicts high detuning-edge-yield using ABAA beyond the 1000-qubit scale. These results demonstrate the cutting-edge capability of frequency targeting using ABAA and open up a new avenue to systematically improving Hamiltonian targeting and optimization for scaling high-performance superconducting quantum processors.
arxiv pdf