Detailed, interpretable characterization of mid-circuit measurement on a transmon qubit

  1. Piper C. Wysocki,
  2. Luke D Burkhart,
  3. Madeline H. Morocco,
  4. Corey I. Ostrove,
  5. Riley J. Murray,
  6. Tristan Brown,
  7. Jeffrey M. Gertler,
  8. David K. Kim,
  9. Nathan E. Miller,
  10. Bethany M. Niedzielski,
  11. Katrina M. Sliwa,
  12. Robin Blume-Kohout,
  13. Gabriel O. Samach,
  14. Mollie E. Schwartz,
  15. and Kenneth M. Rudinger
Mid-circuit measurements (MCMs) are critical components of the quantum error correction protocols expected to enable utility-scale quantum computing. MCMs can be modeled by quantum
instruments (a type of quantum operation or process), which can be characterized self-consistently using gate set tomography. However, experimentally estimated quantum instruments are often hard to interpret or relate to device physics. We address this challenge by adapting the error generator formalism — previously used to interpret noisy quantum gates by decomposing their error processes into physically meaningful sums of „elementary errors“ — to MCMs. We deploy our new analysis on a transmon qubit device to tease out and quantify error mechanisms including amplitude damping, readout error, and imperfect collapse. We examine in detail how the magnitudes of these errors vary with the readout pulse amplitude, recover the key features of dispersive readout predicted by theory, and show that these features can be modeled parsimoniously using a reduced model with just a few parameters.

Experimental Characterization of Crosstalk Errors with Simultaneous Gate Set Tomography

  1. Kenneth Rudinger,
  2. Craig W. Hogle,
  3. Ravi K. Naik,
  4. Akel Hashim,
  5. Daniel Lobser,
  6. David I. Santiago,
  7. Matthew D. Grace,
  8. Erik Nielsen,
  9. Timothy Proctor,
  10. Stefan Seritan,
  11. Susan M. Clark,
  12. Robin Blume-Kohout,
  13. Irfan Siddiqi,
  14. and Kevin C. Young
Crosstalk is a leading source of failure in multiqubit quantum information processors. It can arise from a wide range of disparate physical phenomena, and can introduce subtle correlations
in the errors experienced by a device. Several hardware characterization protocols are able to detect the presence of crosstalk, but few provide sufficient information to distinguish various crosstalk errors from one another. In this article we describe how gate set tomography, a protocol for detailed characterization of quantum operations, can be used to identify and characterize crosstalk errors in quantum information processors. We demonstrate our methods on a two-qubit trapped-ion processor and a two-qubit subsystem of a superconducting transmon processor.

Characterizing mid-circuit measurements on a superconducting qubit using gate set tomography

  1. Kenneth Rudinger,
  2. Guilhem J. Ribeill,
  3. Luke C.G. Govia,
  4. Matthew Ware,
  5. Erik Nielsen,
  6. Kevin Young,
  7. Thomas A. Ohki,
  8. Robin Blume-Kohout,
  9. and Timothy Proctor
Measurements that occur within the internal layers of a quantum circuit — mid-circuit measurements — are an important quantum computing primitive, most notably for quantum
error correction. Mid-circuit measurements have both classical and quantum outputs, so they can be subject to error modes that do not exist for measurements that terminate quantum circuits. Here we show how to characterize mid-circuit measurements, modelled by quantum instruments, using a technique that we call quantum instrument linear gate set tomography (QILGST). We then apply this technique to characterize a dispersive measurement on a superconducting transmon qubit within a multiqubit system. By varying the delay time between the measurement pulse and subsequent gates, we explore the impact of residual cavity photon population on measurement error. QILGST can resolve different error modes and quantify the total error from a measurement; in our experiment, for delay times above 1000 ns we measured a total error rate (i.e., half diamond distance) of ϵ⋄=8.1±1.4%, a readout fidelity of 97.0±0.3%, and output quantum state fidelities of 96.7±0.6% and 93.7±0.7% when measuring 0 and 1, respectively.