W. Livingston

Multipartite Entanglement in Rabi Driven Superconducting Qubits

  1. M. Lu,
  2. J. L. Ville,
  3. J. Cohen,
  4. A. Petrescu,
  5. S. Schreppler,
  6. L. Chen,
  7. C. Jüenger,
  8. C. Pelletti,
  9. A. Marchenkov,
  10. A. Banerjee,
  11. W. Livingston,
  12. J.M. Kreikebaum,
  13. D. Santiago,
  14. A. Blais,
  15. and I. Siddiqi
Exploring highly connected networks of qubits is invaluable for implementing various quantum algorithms and simulations as it allows for entangling qubits with reduced circuit depth.
Here, we demonstrate a multi-qubit STAR (Sideband Tone Assisted Rabi driven) gate. Our scheme is inspired by the ion qubit Mølmer-Sørensen gate and is mediated by a shared photonic mode and Rabi-driven superconducting qubits, which relaxes restrictions on qubit frequencies during fabrication and supports scalability. We achieve a two-qubit gate with maximum state fidelity of 0.95 in 310 ns, a three-qubit gate with state fidelity 0.905\% in 217 ns, and a four-qubit gate with state fidelity 0.66 in 200 ns. Furthermore, we develop a model of the gate that show the four-qubit gate is limited by shared resonator losses and the spread of qubit-resonator couplings, which must be addressed to reach high-fidelity operations.
arxiv pdf

Monitoring fast superconducting qubit dynamics using a neural network

  1. G. Koolstra,
  2. N. Stevenson,
  3. S. Barzili,
  4. L. Burns,
  5. K. Siva,
  6. S. Greenfield,
  7. W. Livingston,
  8. A. Hashim,
  9. R. K. Naik,
  10. J.M. Kreikebaum,
  11. K. P. O'Brien,
  12. D. I. Santiago,
  13. J. Dressel,
  14. and I. Siddiqi
Weak measurements of a superconducting qubit produce noisy voltage signals that are weakly correlated with the qubit state. To recover individual quantum trajectories from these noisy
signals, traditional methods require slow qubit dynamics and substantial prior information in the form of calibration experiments. Monitoring rapid qubit dynamics, e.g. during quantum gates, requires more complicated methods with increased demand for prior information. Here, we experimentally demonstrate an alternative method for accurately tracking rapidly driven superconducting qubit trajectories that uses a Long-Short Term Memory (LSTM) artificial neural network with minimal prior information. Despite few training assumptions, the LSTM produces trajectories that include qubit-readout resonator correlations due to a finite detection bandwidth. In addition to revealing rotated measurement eigenstates and a reduced measurement rate in agreement with theory for a fixed drive, the trained LSTM also correctly reconstructs evolution for an unknown drive with rapid modulation. Our work enables new applications of weak measurements with faster or initially unknown qubit dynamics, such as the diagnosis of coherent errors in quantum gates.
arxiv pdf