Measurement-Induced State Transitions in a Superconducting Qubit: Within the Rotating Wave Approximation

  1. Mostafa Khezri,
  2. Alex Opremcak,
  3. Zijun Chen,
  4. Andreas Bengtsson,
  5. Theodore White,
  6. Ofer Naaman,
  7. Rajeev Acharya,
  8. Kyle Anderson,
  9. Markus Ansmann,
  10. Frank Arute,
  11. Kunal Arya,
  12. Abraham Asfaw,
  13. Joseph C Bardin,
  14. Alexandre Bourassa,
  15. Jenna Bovaird,
  16. Leon Brill,
  17. Bob B. Buckley,
  18. David A. Buell,
  19. Tim Burger,
  20. Brian Burkett,
  21. Nicholas Bushnell,
  22. Juan Campero,
  23. Ben Chiaro,
  24. Roberto Collins,
  25. Alexander L. Crook,
  26. Ben Curtin,
  27. Sean Demura,
  28. Andrew Dunsworth,
  29. Catherine Erickson,
  30. Reza Fatemi,
  31. Vinicius S. Ferreira,
  32. Leslie Flores-Burgos,
  33. Ebrahim Forati,
  34. Brooks Foxen,
  35. Gonzalo Garcia,
  36. William Giang,
  37. Marissa Giustina,
  38. Raja Gosula,
  39. Alejandro Grajales Dau,
  40. Michael C. Hamilton,
  41. Sean D. Harrington,
  42. Paula Heu,
  43. Jeremy Hilton,
  44. Markus R. Hoffmann,
  45. Sabrina Hong,
  46. Trent Huang,
  47. Ashley Huff,
  48. Justin Iveland,
  49. Evan Jeffrey,
  50. Julian Kelly,
  51. Seon Kim,
  52. Paul V. Klimov,
  53. Fedor Kostritsa,
  54. John Mark Kreikebaum,
  55. David Landhuis,
  56. Pavel Laptev,
  57. Lily Laws,
  58. Kenny Lee,
  59. Brian J. Lester,
  60. Alexander T. Lill,
  61. Wayne Liu,
  62. Aditya Locharla,
  63. Erik Lucero,
  64. Steven Martin,
  65. Matt McEwen,
  66. Anthony Megrant,
  67. Xiao Mi,
  68. Kevin C. Miao,
  69. Shirin Montazeri,
  70. Alexis Morvan,
  71. Matthew Neeley,
  72. Charles Neill,
  73. Ani Nersisyan,
  74. Jiun How Ng,
  75. Anthony Nguyen,
  76. Murray Nguyen,
  77. Rebecca Potter,
  78. Chris Quintana,
  79. Charles Rocque,
  80. Pedram Roushan,
  81. Kannan Sankaragomathi,
  82. Kevin J. Satzinger,
  83. Christopher Schuster,
  84. Michael J. Shearn,
  85. Aaron Shorter,
  86. Vladimir Shvarts,
  87. Jindra Skruzny,
  88. W. Clarke Smith,
  89. George Sterling,
  90. Marco Szalay,
  91. Douglas Thor,
  92. Alfredo Torres,
  93. Bryan W. K. Woo,
  94. Z. Jamie Yao,
  95. Ping Yeh,
  96. Juhwan Yoo,
  97. Grayson Young,
  98. Ningfeng Zhu,
  99. Nicholas Zobrist,
  100. and Daniel Sank
Superconducting qubits typically use a dispersive readout scheme, where a resonator is coupled to a qubit such that its frequency is qubit-state dependent. Measurement is performed
by driving the resonator, where the transmitted resonator field yields information about the resonator frequency and thus the qubit state. Ideally, we could use arbitrarily strong resonator drives to achieve a target signal-to-noise ratio in the shortest possible time. However, experiments have shown that when the average resonator photon number exceeds a certain threshold, the qubit is excited out of its computational subspace, which we refer to as a measurement-induced state transition. These transitions degrade readout fidelity, and constitute leakage which precludes further operation of the qubit in, for example, error correction. Here we study these transitions using a transmon qubit by experimentally measuring their dependence on qubit frequency, average photon number, and qubit state, in the regime where the resonator frequency is lower than the qubit frequency. We observe signatures of resonant transitions between levels in the coupled qubit-resonator system that exhibit noisy behavior when measured repeatedly in time. We provide a semi-classical model of these transitions based on the rotating wave approximation and use it to predict the onset of state transitions in our experiments. Our results suggest the transmon is excited to levels near the top of its cosine potential following a state transition, where the charge dispersion of higher transmon levels explains the observed noisy behavior of state transitions. Moreover, occupation in these higher energy levels poses a major challenge for fast qubit reset.

Readout of a quantum processor with high dynamic range Josephson parametric amplifiers

  1. T. C. White,
  2. Alex Opremcak,
  3. George Sterling,
  4. Alexander Korotkov,
  5. Daniel Sank,
  6. Rajeev Acharya,
  7. Markus Ansmann,
  8. Frank Arute,
  9. Kunal Arya,
  10. Joseph C Bardin,
  11. Andreas Bengtsson,
  12. Alexandre Bourassa,
  13. Jenna Bovaird,
  14. Leon Brill,
  15. Bob B. Buckley,
  16. David A. Buell,
  17. Tim Burger,
  18. Brian Burkett,
  19. Nicholas Bushnell,
  20. Zijun Chen,
  21. Ben Chiaro,
  22. Josh Cogan,
  23. Roberto Collins,
  24. Alexander L. Crook,
  25. Ben Curtin,
  26. Sean Demura,
  27. Andrew Dunsworth,
  28. Catherine Erickson,
  29. Reza Fatemi,
  30. Leslie Flores-Burgos,
  31. Ebrahim Forati,
  32. Brooks Foxen,
  33. William Giang,
  34. Marissa Giustina,
  35. Alejandro Grajales Dau,
  36. Michael C. Hamilton,
  37. Sean D. Harrington,
  38. Jeremy Hilton,
  39. Markus Hoffmann,
  40. Sabrina Hong,
  41. Trent Huang,
  42. Ashley Huff,
  43. Justin Iveland,
  44. Evan Jeffrey,
  45. Mária Kieferová,
  46. Seon Kim,
  47. Paul V. Klimov,
  48. Fedor Kostritsa,
  49. John Mark Kreikebaum,
  50. David Landhuis,
  51. Pavel Laptev,
  52. Lily Laws,
  53. Kenny Lee,
  54. Brian J. Lester,
  55. Alexander Lill,
  56. Wayne Liu,
  57. Aditya Locharla,
  58. Erik Lucero,
  59. Trevor McCourt,
  60. Matt McEwen,
  61. Xiao Mi,
  62. Kevin C. Miao,
  63. Shirin Montazeri,
  64. Alexis Morvan,
  65. Matthew Neeley,
  66. Charles Neill,
  67. Ani Nersisyan,
  68. Jiun How Ng,
  69. Anthony Nguyen,
  70. Murray Nguyen,
  71. Rebecca Potter,
  72. Chris Quintana,
  73. Pedram Roushan,
  74. Kannan Sankaragomathi,
  75. Kevin J. Satzinger,
  76. Christopher Schuster,
  77. Michael J. Shearn,
  78. Aaron Shorter,
  79. Vladimir Shvarts,
  80. Jindra Skruzny,
  81. W. Clarke Smith,
  82. Marco Szalay,
  83. Alfredo Torres,
  84. Bryan Woo,
  85. Z. Jamie Yao,
  86. Ping Yeh,
  87. Juhwan Yoo,
  88. Grayson Young,
  89. Ningfeng Zhu,
  90. Nicholas Zobrist,
  91. Yu Chen,
  92. Anthony Megrant,
  93. Julian Kelly,
  94. and Ofer Naaman
We demonstrate a high dynamic range Josephson parametric amplifier (JPA) in which the active nonlinear element is implemented using an array of rf-SQUIDs. The device is matched to the
50 Ω environment with a Klopfenstein-taper impedance transformer and achieves a bandwidth of 250-300 MHz, with input saturation powers up to -95 dBm at 20 dB gain. A 54-qubit Sycamore processor was used to benchmark these devices, providing a calibration for readout power, an estimate of amplifier added noise, and a platform for comparison against standard impedance matched parametric amplifiers with a single dc-SQUID. We find that the high power rf-SQUID array design has no adverse effect on system noise, readout fidelity, or qubit dephasing, and we estimate an upper bound on amplifier added noise at 1.6 times the quantum limit. Lastly, amplifiers with this design show no degradation in readout fidelity due to gain compression, which can occur in multi-tone multiplexed readout with traditional JPAs.

Learning Non-Markovian Quantum Noise from Moiré-Enhanced Swap Spectroscopy with Deep Evolutionary Algorithm

  1. Murphy Yuezhen Niu,
  2. Vadim Smelyanskyi,
  3. Paul Klimov,
  4. Sergio Boixo,
  5. Rami Barends,
  6. Julian Kelly,
  7. Yu Chen,
  8. Kunal Arya,
  9. Brian Burkett,
  10. Dave Bacon,
  11. Zijun Chen,
  12. Ben Chiaro,
  13. Roberto Collins,
  14. Andrew Dunsworth,
  15. Brooks Foxen,
  16. Austin Fowler,
  17. Craig Gidney,
  18. Marissa Giustina,
  19. Rob Graff,
  20. Trent Huang,
  21. Evan Jeffrey,
  22. David Landhuis,
  23. Erik Lucero,
  24. Anthony Megrant,
  25. Josh Mutus,
  26. Xiao Mi,
  27. Ofer Naaman,
  28. Matthew Neeley,
  29. Charles Neill,
  30. Chris Quintana,
  31. Pedram Roushan,
  32. John M. Martinis,
  33. and Hartmut Neven
Two-level-system (TLS) defects in amorphous dielectrics are a major source of noise and decoherence in solid-state qubits. Gate-dependent non-Markovian errors caused by TLS-qubit coupling
are detrimental to fault-tolerant quantum computation and have not been rigorously treated in the existing literature. In this work, we derive the non-Markovian dynamics between TLS and qubits during a SWAP-like two-qubit gate and the associated average gate fidelity for frequency-tunable Transmon qubits. This gate dependent error model facilitates using qubits as sensors to simultaneously learn practical imperfections in both the qubit’s environment and control waveforms. We combine the-state-of-art machine learning algorithm with Moiré-enhanced swap spectroscopy to achieve robust learning using noisy experimental data. Deep neural networks are used to represent the functional map from experimental data to TLS parameters and are trained through an evolutionary algorithm. Our method achieves the highest learning efficiency and robustness against experimental imperfections to-date, representing an important step towards in-situ quantum control optimization over environmental and control defects.

Strong environmental coupling in a Josephson parametric amplifier

  1. Josh Mutus,
  2. Ted White,
  3. Rami Barends,
  4. Yu Chen,
  5. Zijun Chen,
  6. Ben Chiaro,
  7. Andrew Dunsworth,
  8. Evan Jeffrey,
  9. Julian Kelly,
  10. Anthony Megrant,
  11. Charles Neill,
  12. Peter O'Malley,
  13. Pedram Roushan,
  14. Daniel Sank,
  15. Amit Vainsencher,
  16. James Wenner,
  17. Kyle Sundqvist,
  18. Andrew Cleland,
  19. and John Martinis
We present a lumped-element Josephson parametric amplifier designed to operate with strong coupling to the environment. In this regime, we observe broadband frequency dependent amplification
with multi-peaked gain profiles. We account for this behaviour using the „pumpistor“ model which allows for frequency dependent variation of the external impedance. Using this understanding, we demonstrate control over gain profiles through changes in the environment impedance at a given frequency. With strong coupling to a suitable external impedance we observe a significant increase in dynamic range, and large amplification bandwidth up to 700 MHz giving near quantum-limited performance.

Fabrication and Characterization of Aluminum Airbridges for Superconducting Microwave Circuits

  1. Zijun Chen,
  2. Anthony Megrant,
  3. Julian Kelly,
  4. Rami Barends,
  5. Joerg Bochmann,
  6. Yu Chen,
  7. Ben Chiaro,
  8. Andrew Dunsworth,
  9. Evan Jeffrey,
  10. Joshua Mutus,
  11. Peter O'Malley,
  12. Charles Neill,
  13. Pedram Roushan,
  14. Daniel Sank,
  15. Amit Vainsencher,
  16. James Wenner,
  17. Theodore White,
  18. Andrew Cleland,
  19. and John Martinis
Superconducting microwave circuits based on coplanar waveguides (CPW) are susceptible to parasitic slotline modes which can lead to loss and decoherence. We motivate the use of superconducting
airbridges as a reliable method for preventing the propagation of these modes. We describe the fabrication of these airbridges on superconducting resonators, which we use to measure the loss due to placing airbridges over CPW lines. We find that the additional loss at single photon levels is small, and decreases at higher drive powers.

Design and characterization of a lumped element single-ended superconducting microwave parametric amplifier with on-chip flux bias line

  1. Josh Mutus,
  2. Ted White,
  3. Evan Jeffery,
  4. Daniel Sank,
  5. Rami Barends,
  6. Joerg Bochmann,
  7. Yu Chen,
  8. Zijun Chen,
  9. Ben Chiaro,
  10. Andrew Dunsworth,
  11. Julian Kelly,
  12. Anthony Megrant,
  13. Charles Neill,
  14. Peter O'malley,
  15. Pedram Roushan,
  16. Amit Vainsencher,
  17. Jim Wenner,
  18. Irfan Siddiqi,
  19. Rajamani Vijayaraghavan,
  20. Andrew Cleland,
  21. and John Martinis
We demonstrate a lumped-element Josephson Parametric Amplifier (LJPA), using a single-ended design that includes an on-chip, high-bandwidth flux bias line. The amplifier can be pumped
into its region of parametric gain through either the input port or through the flux bias line. Broadband amplification is achieved at a tunable frequency $\omega/2 \pi$ between 5 to 7 GHz with quantum-limited noise performance, a gain-bandwidth product greater than 500 MHz, and an input saturation power in excess of -120 dBm. The bias line allows fast frequency tuning of the amplifier, with variations of hundreds of MHz over time scales shorter than 10 ns.

Sputtered TiN films for superconducting coplanar waveguide resonators

  1. Shinobu Ohya,
  2. Ben Chiaro,
  3. Anthony Megrant,
  4. Charles Neill,
  5. Rami Barends,
  6. Yu Chen,
  7. Julian Kelly,
  8. David Low,
  9. Josh Mutus,
  10. Peter O'Malley,
  11. Pedram Roushan,
  12. Daniel Sank,
  13. Amit Vainsencher,
  14. James Wenner,
  15. Theodore C. White,
  16. Yi Yin,
  17. B. D. Schultz,
  18. Chris J Palmstrøm,
  19. Benjamin A. Mazin,
  20. Andrew N. Cleland,
  21. and John M. Martinis
We present a systematic study of the properties of TiN films by varying the deposition conditions in an ultra-high-vacuum reactive magnetron sputtering chamber. By increasing the deposition
pressure from 2 to 9 mTorr while keeping a nearly stoichiometric composition of Ti(1-x)N(x) (x=0.5), the film resistivity increases, the dominant crystal orientation changes from (100) to (111), grain boundaries become clearer, and the strong compressive strain changes to weak tensile strain. The TiN films absorb a high concentration of contaminants including hydrogen, carbon, and oxygen when they are exposed to air after deposition. With the target-substrate distance set to 88 mm the contaminant levels increase from ~0.1% to ~10% as the pressure is increased from 2 to 9 mTorr. The contaminant concentrations also correlate with in-plane distance from the center of the substrate and increase by roughly two orders of magnitude as the target-substrate distance is increased from 88 mm to 266 mm. These contaminants are found to strongly influence the properties of TiN films. For instance, the resistivity of stoichiometric films increases by around a factor of 5 as the oxygen content increases from 0.1% to 11%. These results suggest that the sputtered TiN particle energy is critical in determining the TiN film properties, and that it is important to control this energy to obtain high-quality TiN films. Superconducting coplanar waveguide resonators made from a series of nearly stoichiometric films grown at pressures from 2 mTorr to 7 mTorr show an increase in intrinsic quality factor from ~10^4 to ~10^6 as the magnitude of the compressive strain decreases from nearly 3800 MPa to approximately 150 MPa and the oxygen content increases from 0.1% to 8%. The films with a higher oxygen content exhibit lower loss, but the nonuniformity of the oxygen incorporation hinders the use of sputtered TiN in larger circuits.