System Characterization of Dispersive Readout in Superconducting Qubits

  1. Daniel Sank,
  2. Alex Opremcak,
  3. Andreas Bengtsson,
  4. Mostafa Khezri,
  5. Zijun Chen,
  6. Ofer Naaman,
  7. and Alexander Korotkov
Designing quantum systems with the measurement speed and accuracy needed for quantum error correction using superconducting qubits requires iterative design and test informed by accurate
models and characterization tools. We introduce a single protocol, with few prerequisite calibrations, which measures the dispersive shift, resonator linewidth, and drive power used in the dispersive readout of superconducting qubits. We find that the resonator linewidth is poorly controlled with a factor of 2 between the maximum and minimum measured values, and is likely to require focused attention in future quantum error correction experiments. We also introduce a protocol for measuring the readout system efficiency using the same power levels as are used in typical qubit readout, and without the need to measure the qubit coherence. We routinely run these protocols on chips with tens of qubits, driven by automation software with little human interaction. Using the extracted system parameters, we find that a model based on those parameters predicts the readout signal to noise ratio to within 10% over a device with 54 qubits.

Josephson parametric circulator with same-frequency signal ports, 200 MHz bandwidth, and high dynamic range

  1. Randy Kwende,
  2. Theodore White,
  3. and Ofer Naaman
We demonstrate a 3-port Josephson parametric circulator, matched to 50 Ohm using second order Chebyshev networks. The device notably operates with two of its signal ports at the same
frequency and uses only two out-of-phase pumps at a single frequency. As a consequence, when operated as an isolator it does not require phase coherence between the pumps and the signal, thus simplifying the requirements for its integration into standard dispersive qubit readout setups. The device utilizes parametric couplers based on a balanced bridge of rf-SQUID arrays, which offer purely parametric coupling and high dynamic range. We characterize the device by measuring its full 3×3 S-matrix as a function of frequency and the relative phase between the two pumps. We find up to 15 dB nonreciprocity over a 200 MHz signal band, port match better than 10 dB, low insertion loss of 0.6 dB, and saturation power exceeding -80 dBm.

Parametric Amplifier Matching Using Legendre Prototypes

  1. Ryan Kaufman,
  2. and Ofer Naaman
In this note we describe Josephson parametric amplifier (JPA) matching networks based on Legendre polynomials. These networks typically exhibit lower ripple and gentler roll-off than
Chebyshev networks with similar parameters, and can be viewed as bridging the gap between Butterworth and Chebyshev ones. We tabulate prototype coefficients for parametric amplifiers based on Legendre polynomials with a range of gain and ripple parameters, and for a range of network orders. We also use this opportunity to further illustrate the synthesis of these networks based on methods from previous work, and synthesize a prototype JPA with 20dB gain at a center frequency of 5GHz with a bandwidth of 500MHz.

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.

Resolving catastrophic error bursts from cosmic rays in large arrays of superconducting qubits

  1. Matt McEwen,
  2. Lara Faoro,
  3. Kunal Arya,
  4. Andrew Dunsworth,
  5. Trent Huang,
  6. Seon Kim,
  7. Brian Burkett,
  8. Austin Fowler,
  9. Frank Arute,
  10. Joseph C Bardin,
  11. Andreas Bengtsson,
  12. Alexander Bilmes,
  13. Bob B. Buckley,
  14. Nicholas Bushnell,
  15. Zijun Chen,
  16. Roberto Collins,
  17. Sean Demura,
  18. Alan R. Derk,
  19. Catherine Erickson,
  20. Marissa Giustina,
  21. Sean D. Harrington,
  22. Sabrina Hong,
  23. Evan Jeffrey,
  24. Julian Kelly,
  25. Paul V. Klimov,
  26. Fedor Kostritsa,
  27. Pavel Laptev,
  28. Aditya Locharla,
  29. Xiao Mi,
  30. Kevin C. Miao,
  31. Shirin Montazeri,
  32. Josh Mutus,
  33. Ofer Naaman,
  34. Matthew Neeley,
  35. Charles Neill,
  36. Alex Opremcak,
  37. Chris Quintana,
  38. Nicholas Redd,
  39. Pedram Roushan,
  40. Daniel Sank,
  41. Kevin J. Satzinger,
  42. Vladimir Shvarts,
  43. Theodore White,
  44. Z. Jamie Yao,
  45. Ping Yeh,
  46. Juhwan Yoo,
  47. Yu Chen,
  48. Vadim Smelyanskiy,
  49. John M. Martinis,
  50. Hartmut Neven,
  51. Anthony Megrant,
  52. Lev Ioffe,
  53. and Rami Barends
Scalable quantum computing can become a reality with error correction, provided coherent qubits can be constructed in large arrays. The key premise is that physical errors can remain
both small and sufficiently uncorrelated as devices scale, so that logical error rates can be exponentially suppressed. However, energetic impacts from cosmic rays and latent radioactivity violate both of these assumptions. An impinging particle ionizes the substrate, radiating high energy phonons that induce a burst of quasiparticles, destroying qubit coherence throughout the device. High-energy radiation has been identified as a source of error in pilot superconducting quantum devices, but lacking a measurement technique able to resolve a single event in detail, the effect on large scale algorithms and error correction in particular remains an open question. Elucidating the physics involved requires operating large numbers of qubits at the same rapid timescales as in error correction, exposing the event’s evolution in time and spread in space. Here, we directly observe high-energy rays impacting a large-scale quantum processor. We introduce a rapid space and time-multiplexed measurement method and identify large bursts of quasiparticles that simultaneously and severely limit the energy coherence of all qubits, causing chip-wide failure. We track the events from their initial localised impact to high error rates across the chip. Our results provide direct insights into the scale and dynamics of these damaging error bursts in large-scale devices, and highlight the necessity of mitigation to enable quantum computing to scale.

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.