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.

A 28nm Bulk-CMOS 4-to-8GHz <2mW Cryogenic Pulse Modulator for Scalable Quantum Computing

  1. Joseph C Bardin,
  2. Evan Jeffrey,
  3. Erik Lucero,
  4. Trent Huang,
  5. Ofer Naaman,
  6. Rami Barends,
  7. Ted White,
  8. Marissa Giustina,
  9. Daniel Sank,
  10. Pedram Roushan,
  11. Kunal Arya,
  12. Benjamin Chiaro,
  13. Julian Kelly,
  14. Jimmy Chen,
  15. Brian Burkett,
  16. Yu Chen,
  17. Andrew Dunsworth,
  18. Austin Fowler,
  19. Brooks Foxen,
  20. Craig Gidney,
  21. Rob Graff,
  22. Paul Klimov,
  23. Josh Mutus,
  24. Matthew McEwen,
  25. Anthony Megrant,
  26. Matthew Neeley,
  27. Charles Neill,
  28. Chris Quintana,
  29. Amit Vainsencher,
  30. Hartmut Neven,
  31. and John Martinis
Future quantum computing systems will require cryogenic integrated circuits to control and measure millions of qubits. In this paper, we report the design and characterization of a
prototype cryogenic CMOS integrated circuit that has been optimized for the control of transmon qubits. The circuit has been integrated into a quantum measurement setup and its performance has been validated through multiple quantum control experiments.