Meghan Schuldt

Implementing a synthetic magnetic vector potential in a 2D superconducting qubit array

  1. Ilan T. Rosen,
  2. Sarah Muschinske,
  3. Cora N. Barrett,
  4. Arkya Chatterjee,
  5. Max Hays,
  6. Michael DeMarco,
  7. Amir Karamlou,
  8. David Rower,
  9. Rabindra Das,
  10. David K. Kim,
  11. Bethany M. Niedzielski,
  12. Meghan Schuldt,
  13. Kyle Serniak,
  14. Mollie E. Schwartz,
  15. Jonilyn L. Yoder,
  16. Jeffrey A. Grover,
  17. and William D. Oliver
Superconducting quantum processors are a compelling platform for analog quantum simulation due to the precision control, fast operation, and site-resolved readout inherent to the hardware.
Arrays of coupled superconducting qubits natively emulate the dynamics of interacting particles according to the Bose-Hubbard model. However, many interesting condensed-matter phenomena emerge only in the presence of electromagnetic fields. Here, we emulate the dynamics of charged particles in an electromagnetic field using a superconducting quantum simulator. We realize a broadly adjustable synthetic magnetic vector potential by applying continuous modulation tones to all qubits. We verify that the synthetic vector potential obeys requisite properties of electromagnetism: a spatially-varying vector potential breaks time-reversal symmetry and generates a gauge-invariant synthetic magnetic field, and a temporally-varying vector potential produces a synthetic electric field. We demonstrate that the Hall effect–the transverse deflection of a charged particle propagating in an electromagnetic field–exists in the presence of the synthetic electromagnetic field.
arxiv pdf

Learning-based Calibration of Flux Crosstalk in Transmon Qubit Arrays

  1. Cora N. Barrett,
  2. Amir H. Karamlou,
  3. Sarah E. Muschinske,
  4. Ilan T. Rosen,
  5. Jochen Braumüller,
  6. Rabindra Das,
  7. David K. Kim,
  8. Bethany M. Niedzielski,
  9. Meghan Schuldt,
  10. Kyle Serniak,
  11. Mollie E. Schwartz,
  12. Jonilyn L. Yoder,
  13. Terry P. Orlando,
  14. Simon Gustavsson,
  15. Jeffrey A. Grover,
  16. and William D. Oliver
Superconducting quantum processors comprising flux-tunable data and coupler qubits are a promising platform for quantum computation. However, magnetic flux crosstalk between the flux-control
lines and the constituent qubits impedes precision control of qubit frequencies, presenting a challenge to scaling this platform. In order to implement high-fidelity digital and analog quantum operations, one must characterize the flux crosstalk and compensate for it. In this work, we introduce a learning-based calibration protocol and demonstrate its experimental performance by calibrating an array of 16 flux-tunable transmon qubits. To demonstrate the extensibility of our protocol, we simulate the crosstalk matrix learning procedure for larger arrays of transmon qubits. We observe an empirically linear scaling with system size, while maintaining a median qubit frequency error below 300 kHz.
arxiv pdf