Resisting high-energy impact events through gap engineering in superconducting qubit arrays

  1. Matt McEwen,
  2. Kevin C. Miao,
  3. Juan Atalaya,
  4. Alex Bilmes,
  5. Alex Crook,
  6. Jenna Bovaird,
  7. John Mark Kreikebaum,
  8. Nicholas Zobrist,
  9. Evan Jeffrey,
  10. Bicheng Ying,
  11. Andreas Bengtsson,
  12. Hung-Shen Chang,
  13. Andrew Dunsworth,
  14. Julian Kelly,
  15. Yaxing Zhang,
  16. Ebrahim Forati,
  17. Rajeev Acharya,
  18. Justin Iveland,
  19. Wayne Liu,
  20. Seon Kim,
  21. Brian Burkett,
  22. Anthony Megrant,
  23. Yu Chen,
  24. Charles Neill,
  25. Daniel Sank,
  26. Michel Devoret,
  27. and Alex Opremcak
Quantum error correction (QEC) provides a practical path to fault-tolerant quantum computing through scaling to large qubit numbers, assuming that physical errors are sufficiently uncorrelated
in time and space. In superconducting qubit arrays, high-energy impact events produce correlated errors, violating this key assumption. Following such an event, phonons with energy above the superconducting gap propagate throughout the device substrate, which in turn generate a temporary surge in quasiparticle (QP) density throughout the array. When these QPs tunnel across the qubits‘ Josephson junctions, they induce correlated errors. Engineering different superconducting gaps across the qubit’s Josephson junctions provides a method to resist this form of QP tunneling. By fabricating all-aluminum transmon qubits with both strong and weak gap engineering on the same substrate, we observe starkly different responses during high-energy impact events. Strongly gap engineered qubits do not show any degradation in T1 during impact events, while weakly gap engineered qubits show events of correlated degradation in T1. We also show that strongly gap engineered qubits are robust to QP poisoning from increasing optical illumination intensity, whereas weakly gap engineered qubits display rapid degradation in coherence. Based on these results, gap engineering removes the threat of high-energy impacts to QEC in superconducting qubit arrays.

Optimizing quantum gates towards the scale of logical qubits

  1. Paul V. Klimov,
  2. Andreas Bengtsson,
  3. Chris Quintana,
  4. Alexandre Bourassa,
  5. Sabrina Hong,
  6. Andrew Dunsworth,
  7. Kevin J. Satzinger,
  8. William P. Livingston,
  9. Volodymyr Sivak,
  10. Murphy Y. Niu,
  11. Trond I. Andersen,
  12. Yaxing Zhang,
  13. Desmond Chik,
  14. Zijun Chen,
  15. Charles Neill,
  16. Catherine Erickson,
  17. Alejandro Grajales Dau,
  18. Anthony Megrant,
  19. Pedram Roushan,
  20. Alexander N. Korotkov,
  21. Julian Kelly,
  22. Vadim Smelyanskiy,
  23. Yu Chen,
  24. and Hartmut Neven
A foundational assumption of quantum error correction theory is that quantum gates can be scaled to large processors without exceeding the error-threshold for fault tolerance. Two major
challenges that could become fundamental roadblocks are manufacturing high performance quantum hardware and engineering a control system that can reach its performance limits. The control challenge of scaling quantum gates from small to large processors without degrading performance often maps to non-convex, high-constraint, and time-dependent control optimization over an exponentially expanding configuration space. Here we report on a control optimization strategy that can scalably overcome the complexity of such problems. We demonstrate it by choreographing the frequency trajectories of 68 frequency-tunable superconducting qubits to execute single- and two-qubit gates while mitigating computational errors. When combined with a comprehensive model of physical errors across our processor, the strategy suppresses physical error rates by ∼3.7× compared with the case of no optimization. Furthermore, it is projected to achieve a similar performance advantage on a distance-23 surface code logical qubit with 1057 physical qubits. Our control optimization strategy solves a generic scaling challenge in a way that can be adapted to other quantum algorithms, operations, and computing architectures.

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.

Tunable coupler for superconducting Xmon qubits: Perturbative nonlinear model

  1. Michael R. Geller,
  2. Emmanuel Donate,
  3. Yu Chen,
  4. Charles Neill,
  5. Pedram Roushan,
  6. and John M. Martinis
We study a recently demonstrated design for a high-performance tunable coupler suitable for superconducting Xmon and planar transmon qubits. The coupler circuit uses a single flux-biased
Josephson junction and acts as a tunable current divider. We calculate the effective qubit-qubit interaction Hamiltonian by treating the nonlinearity of the qubit and coupler junctions perturbatively. We find that the qubit nonlinearity has two principal effects: The first is to suppress the magnitude of the transverse XX coupling from that obtained in the harmonic approximation by about 15%. The second is to induce a small diagonal ZZ coupling. The effects of the coupler junction nonlinearity are negligible in the parameter regime considered.

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.