E. Schuyler Fried

Assessing the Influence of Broadband Instrumentation Noise on Parametrically Modulated Superconducting Qubits

  1. E. Schuyler Fried,
  2. Prasahnt Sivarajah,
  3. Nicolas Didier,
  4. Eyob A. Sete,
  5. Marcus P. da Silva,
  6. Blake R. Johnson,
  7. and Colm A. Ryan
With superconducting transmon qubits — a promising platform for quantum information processing — two-qubit gates can be performed using AC signals to modulate a tunable
transmon’s frequency via magnetic flux through its SQUID loop. However, frequency tunablity introduces an additional dephasing mechanism from magnetic fluctuations. In this work, we experimentally study the contribution of instrumentation noise to flux instability and the resulting error rate of parametrically activated two-qubit gates. Specifically, we measure the qubit coherence time under flux modulation while injecting broadband noise through the flux control channel. We model the noise’s effect using a dephasing rate model that matches well to the measured rates, and use it to prescribe a noise floor required to achieve a desired two-qubit gate infidelity. Finally, we demonstrate that low-pass filtering the AC signal used to drive two-qubit gates between the first and second harmonic frequencies can reduce qubit sensitivity to flux noise at the AC sweet spot (ACSS), confirming an earlier theoretical prediction. The framework we present to determine instrumentation noise floors required for high entangling two-qubit gate fidelity should be extensible to other quantum information processing systems.
arxiv pdf

Unsupervised Machine Learning on a Hybrid Quantum Computer

  1. J. S. Otterbach,
  2. R. Manenti,
  3. N. Alidoust,
  4. A. Bestwick,
  5. M. Block,
  6. B. Bloom,
  7. S. Caldwell,
  8. N. Didier,
  9. E. Schuyler Fried,
  10. S. Hong,
  11. P. Karalekas,
  12. C. B. Osborn,
  13. A. Papageorge,
  14. E. C. Peterson,
  15. G. Prawiroatmodjo,
  16. N. Rubin,
  17. Colm A. Ryan,
  18. D. Scarabelli,
  19. M. Scheer,
  20. E. A. Sete,
  21. P. Sivarajah,
  22. Robert S. Smith,
  23. A. Staley,
  24. N. Tezak,
  25. W. J. Zeng,
  26. A. Hudson,
  27. Blake R. Johnson,
  28. M. Reagor,
  29. M. P. da Silva,
  30. and C. Rigetti
Machine learning techniques have led to broad adoption of a statistical model of computing. The statistical distributions natively available on quantum processors are a superset of
those available classically. Harnessing this attribute has the potential to accelerate or otherwise improve machine learning relative to purely classical performance. A key challenge toward that goal is learning to hybridize classical computing resources and traditional learning techniques with the emerging capabilities of general purpose quantum processors. Here, we demonstrate such hybridization by training a 19-qubit gate model processor to solve a clustering problem, a foundational challenge in unsupervised learning. We use the quantum approximate optimization algorithm in conjunction with a gradient-free Bayesian optimization to train the quantum machine. This quantum/classical hybrid algorithm shows robustness to realistic noise, and we find evidence that classical optimization can be used to train around both coherent and incoherent imperfections.
arxiv pdf