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Demonstration of the trapped-ion quantum CCD computer architecture

Abstract

The trapped-ion quantum charge-coupled device (QCCD) proposal1,2 lays out a blueprint for a universal quantum computer that uses mobile ions as qubits. Analogous to a charge-coupled device (CCD) camera, which stores and processes imaging information as movable electrical charges in coupled pixels, a QCCD computer stores quantum information in the internal state of electrically charged ions that are transported between different processing zones using dynamic electric fields. The promise of the QCCD architecture is to maintain the low error rates demonstrated in small trapped-ion experiments3,4,5 by limiting the quantum interactions to multiple small ion crystals, then physically splitting and rearranging the constituent ions of these crystals into new crystals, where further interactions occur. This approach leverages transport timescales that are fast relative to the coherence times of the qubits, the insensitivity of the qubit states of the ion to the electric fields used for transport, and the low crosstalk afforded by spatially separated crystals. However, engineering a machine capable of executing these operations across multiple interaction zones with low error introduces many difficulties, which have slowed progress in scaling this architecture to larger qubit numbers. Here we use a cryogenic surface trap to integrate all necessary elements of the QCCD architecture—a scalable trap design, parallel interaction zones and fast ion transport—into a programmable trapped-ion quantum computer that has a system performance consistent with the low error rates achieved in the individual ion crystals. We apply this approach to realize a teleported CNOT gate using mid-circuit measurement6, negligible crosstalk error and a quantum volume7 of 26 = 64. These results demonstrate that the QCCD architecture provides a viable path towards high-performance quantum computers.

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Fig. 1: The programmable QCCD quantum computing system.
Fig. 2: Transport and timing for an N = 6 QV circuit.
Fig. 3: QV circuits and results.

Data availability

The data presented in this manuscript are available from the corresponding author upon reasonable request.

References

  1. 1.

    Wineland, D. J. et al. Experimental issues in coherent quantum-state manipulation of trapped atomic ions. J. Res. Natl. Inst. Stand. Technol. 103, 259–328 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Kielpinski, D., Monroe, C. & Wineland, D. J. Architecture for a large-scale ion-trap quantum computer. Nature 417, 709–711 (2002).

    ADS  CAS  PubMed  Google Scholar 

  3. 3.

    Gaebler, J. P. et al. High-fidelity universal gate set for 9Be+ ion qubits. Phys. Rev. Lett. 117, 060505 (2016).

    ADS  CAS  PubMed  Google Scholar 

  4. 4.

    Ballance, C. J., Harty, T. P., Linke, N. M., Sepiol, M. A. & Lucas, D. M. High-fidelity quantum logic gates using trapped-ion hyperfine qubits. Phys. Rev. Lett. 117, 060504 (2016).

    ADS  CAS  PubMed  Google Scholar 

  5. 5.

    Christensen, J. E., Hucul, D., Campbell, W. C. & Hudson, E. R. High-fidelity manipulation of a qubit enabled by a manufactured nucleus. npj Quant. Inf. 6, 35 (2020).

    ADS  Google Scholar 

  6. 6.

    Wan, Y. et al. Quantum gate teleportation between separated qubits in a trapped-ion processor. Science 364, 875–878 (2019).

    ADS  CAS  PubMed  Google Scholar 

  7. 7.

    Cross, A. W., Bishop, L. S., Sheldon, S., Nation, P. D. & Gambetta, J. M. Validating quantum computers using randomized model circuits. Phys. Rev. A 100, 032328 (2019).

    ADS  CAS  Google Scholar 

  8. 8.

    Cirac, J. I. & Zoller, P. Quantum computations with cold trapped ions. Phys. Rev. Lett. 74, 4091–4094 (1995).

    ADS  CAS  PubMed  Google Scholar 

  9. 9.

    Monroe, C., Meekhof, D. M., King, B. E., Itano, W. M. & Wineland, D. J. Demonstration of a fundamental quantum logic gate. Phys. Rev. Lett. 75, 4714–4717 (1995).

    ADS  MathSciNet  CAS  PubMed  MATH  Google Scholar 

  10. 10.

    Wang, Y. et al. Single-qubit quantum memory exceeding ten-minute coherence time. Nat. Photon. 11, 646–650 (2017).

    ADS  CAS  Google Scholar 

  11. 11.

    Murali, P., Debroy, D. M., Brown, K. R. & Martonosi, M. Architecting noisy intermediate-scale trapped ion quantum computers. In 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA) 529–542 (IEEE, 2020).

  12. 12.

    Monroe, C. et al. Large-scale modular quantum-computer architecture with atomic memory and photonic interconnects. Phys. Rev. A 89, 022317 (2014).

    ADS  Google Scholar 

  13. 13.

    Hucul, D. et al. Modular entanglement of atomic qubits using photons and phonons. Nat. Phys. 11, 37–42 (2015).

    CAS  Google Scholar 

  14. 14.

    Home, J. P. et al. Complete methods set for scalable ion trap quantum information processing. Science 325, 1227–1230 (2009).

    ADS  MathSciNet  CAS  PubMed  MATH  Google Scholar 

  15. 15.

    Kaufmann, H. et al. Scalable creation of long-lived multipartite entanglement. Phys. Rev. Lett. 119, 150503 (2017).

    ADS  MathSciNet  CAS  PubMed  Google Scholar 

  16. 16.

    Lekitsch, B. et al. Blueprint for a microwave trapped ion quantum computer. Sci. Adv. 3, e1601540 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Labaziewicz, J. High Fidelity Quantum Gates with Ions in Cryogenic Microfabricated Ion Traps. PhD thesis, MIT (2008); http://web.mit.edu/cua/www/quanta/LabaziewiczThesis.pdf

  18. 18.

    Maunz, P. L. W. High Optical Access Trap 2.0. Report SAND2016–0796R https://prod-ng.sandia.gov/techlib-noauth/access-control.cgi/2016/160796r.pdf (Sandia National Laboratories, 2016).

  19. 19.

    Bowler, R. et al. Coherent diabatic ion transport and separation in a multizone trap array. Phys. Rev. Lett. 109, 080502 (2012).

    ADS  CAS  PubMed  Google Scholar 

  20. 20.

    Kaushal, V. et al. Shuttling-based trapped-ion quantum information processing. AVS Quantum Sci. 2, 014101 (2020).

    ADS  Google Scholar 

  21. 21.

    Barrett, M. D. et al. Sympathetic cooling of 9Be+ and 24Mg+ for quantum logic. Phys. Rev. A 68, 042302 (2003).

    ADS  Google Scholar 

  22. 22.

    Home, J. P. et al. Memory coherence of a sympathetically cooled trapped-ion qubit. Phys. Rev. A 79, 050305 (2009).

    ADS  Google Scholar 

  23. 23.

    Nielsen, M. A. & Chuang, I. L. Quantum Computation and Quantum Information (Cambridge Univ. Press, 2000).

  24. 24.

    Palmero, M., Bowler, R., Gaebler, J. P., Leibfried, D. & Muga, J. G. Fast transport of mixed-species ion chains within a paul trap. Phys. Rev. A 90, 053408 (2014).

    ADS  Google Scholar 

  25. 25.

    Home, J. P. & Steane, A. M. Electrode configurations for fast separation of trapped ions. Quantum Inf. Comput. 6, 289–325 (2006).

    CAS  MATH  Google Scholar 

  26. 26.

    Splatt, F. et al. Deterministic reordering of 40Ca+ ions in a linear segmented Paul trap. New J. Phys. 11, 103008 (2009).

    ADS  Google Scholar 

  27. 27.

    Haberman, N. Parallel Neighbor Sort (or the Glory of the Induction Principle). CMU Computer Science Report https://kilthub.cmu.edu/articles/journal_contribution/Parallel_neighbor-sort_or_the_glory_of_the_induction_principle_/6608258 (Carnegie Mellon University, 1979).

  28. 28.

    Sørensen, A. & Mølmer, K. Entanglement and quantum computation with ions in thermal motion. Phys. Rev. A 62, 022311 (2000).

    ADS  Google Scholar 

  29. 29.

    Lee, P. J. et al. Phase control of trapped ion quantum gates. J. Opt. B 7, S371–S383 (2005).

    CAS  Google Scholar 

  30. 30.

    Baldwin, C. H., Bjork, B. J., Gaebler, J. P., Hayes, D. & Stack, D. Subspace benchmarking high-fidelity entangling operations with trapped ions. Phys. Rev. Res. 2, 013317 (2020).

    CAS  Google Scholar 

  31. 31.

    Viola, L., Knill, E. & Lloyd, S. Dynamical decoupling of open quantum systems. Phys. Rev. Lett. 82, 2417–2421 (1999).

    ADS  MathSciNet  CAS  MATH  Google Scholar 

  32. 32.

    Olmschenk, S. et al. Manipulation and detection of a trapped Yb+ hyperfine qubit. Phys. Rev. A 76, 052314 (2007).

    ADS  Google Scholar 

  33. 33.

    Magesan, E., Gambetta, J. M. & Emerson, J. Scalable and robust randomized benchmarking of quantum processes. Phys. Rev. Lett. 106, 180504 (2011).

    ADS  PubMed  Google Scholar 

  34. 34.

    Monroe, C. et al. Resolved-sideband raman cooling of a bound atom to the 3D zero-point energy. Phys. Rev. Lett. 75, 4011–4014 (1995).

    ADS  CAS  PubMed  Google Scholar 

  35. 35.

    Jordan, E. et al. Near ground-state cooling of two-dimensional trapped-ion crystals with more than 100 ions. Phys. Rev. Lett. 122, 053603 (2019).

    ADS  CAS  PubMed  Google Scholar 

  36. 36.

    Gambetta, J. M. et al. Characterization of addressability by simultaneous randomized benchmarking. Phys. Rev. Lett. 109, 240504 (2012).

    ADS  PubMed  Google Scholar 

  37. 37.

    Barrett, M. et al. Deterministic quantum teleportation of atomic qubits. Nature 429, 737–739 (2004).

    ADS  CAS  PubMed  Google Scholar 

  38. 38.

    Negnevitsky, V. et al. Repeated multi-qubit readout and feedback with a mixed species trapped-ion register. Nature 563, 527–531 (2018).

    ADS  CAS  PubMed  Google Scholar 

  39. 39.

    Eisert, J., Jacobs, K., Papadopoulos, P. & Plenio, M. B. Optimal local implementation of nonlocal quantum gates. Phys. Rev. A 62, 052317 (2000).

    ADS  Google Scholar 

  40. 40.

    McClean, J. R., Romero, J., Babbush, R. & Aspuru-Guzik, A. The theory of variational hybrid quantum-classical algorithms. New J. Phys. 18, 023023 (2016).

    ADS  MATH  Google Scholar 

  41. 41.

    Farhi, E. & Goldstone, J. A quantum approximate optimization algorithm. Preprint at https://arxiv.org/abs/1411.4028 (2014).

  42. 42.

    Aaronson, S. & Chen, L. Complexity-theoretic foundations of quantum supremacy experiments. Preprint at https://arxiv.org/abs/1612.05903 (2016).

  43. 43.

    Jucevic, P. et al. Demonstration of quantum volume 64 on a superconducting quantum computing system. Preprint at https://arxiv.org/abs/2008.08571 (2020).

  44. 44.

    van Mourik, M. W. et al. Coherent rotations of qubits within a a surface ion-trap quantum computer. Phys. Rev. A 102, 022611 (2020).

    ADS  Google Scholar 

  45. 45.

    Mount, E. et al. Single qubit manipulation in a microfabricated surface electrode ion trap. New J. Phys. 15, 093018 (2013).

    ADS  CAS  Google Scholar 

  46. 46.

    Mehta, K. K. et al. Integrated optical multi-ion quantum logic. Nature 586, 533–537 (2020).

    ADS  CAS  PubMed  Google Scholar 

  47. 47.

    Kovalev, A. A. & Pryadko, L. P. Quantum kronecker sum-product low-density parity-check codes with finite rate. Phys. Rev. A 88, 012311 (2013).

    ADS  Google Scholar 

  48. 48.

    Blakestad, R. B. Transport of Trapped-ion Qubits within a Scalable Quantum Processor. PhD thesis, Univ. of Colorado (2010); https://www.nist.gov/system/files/documents/2017/05/09/blakestad2010thesis.pdf.

  49. 49.

    Biercuk, M. J., Doherty, A. C. & Uys, H. Dynamical decoupling sequence construction as a filter-design problem. J. Phys. B 44, 154002 (2011).

    ADS  Google Scholar 

  50. 50.

    Harper, R., Flammia, S. T. & Wallman, J. J. Efficient learning of quantum noise. Nat. Phys. 16, 1184–1188 (2020).

    Google Scholar 

  51. 51.

    Meier, A. M. Randomized Benchmarking of Clifford Operators. PhD thesis, Univ. of Colorado (2006); https://arxiv.org/abs/1811.10040

  52. 52.

    Hofmann, H. F. Complementary classical fidelities as an efficient criterion for the evaluation of experimentally realized quantum operations. Phys. Rev. Lett. 94, 160504 (2005).

    ADS  MathSciNet  PubMed  Google Scholar 

  53. 53.

    Nielsen, M. A. A simple formula for the average gate fidelity of a quantum dynamical operation. Phys. Lett. A 303, 249–252 (2002).

    ADS  MathSciNet  CAS  MATH  Google Scholar 

  54. 54.

    Abraham, H. et al. Qiskit: an Open-Source Framework for Quantum Computing https://zenodo.org/record/2562111#.YC6b8n7LdaR (2019).

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Acknowledgements

This work was made possible by a large group of people, and we thank the entire Honeywell Quantum Solutions team for their many contributions.

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Contributions

J.M.P., J.M.D., C.F., J.P.G., S.A.M., M.S.A. and B.N. all contributed to the experimental design, construction and data collection. C.H.B. and K.M. contributed to the theory, quantum circuit design and data analysis. All authors contributed to this manuscript.

Corresponding author

Correspondence to D. Hayes.

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The authors declare no competing interests.

Additional information

Extended data

is available for this paper at https://doi.org/10.1038/s41586-021-03318-4.

Peer review information Nature thanks Winfried Hensinger and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 System details.

a, Close-up schematic of the trap region used in this work. Dimensions are in micrometres. We refer to the orange gating region on the left as zone 1 and the blue gating region as zone 2. b, Transport primitive library and associated heating estimates. For the estimates, we fit four ion-crystal spin-flip data to a model that assumes that all modes are at the same temperature. The fitted temperature increases are converted to units of quanta/mode (we note that the inequality holds for all modes). The times shown do not include interpolation between different operations or small delays in the electronics, which increase the time for every operation by ~10%. The interzone shift is a linear shift between the two gate zones, whereas an intrazone shift moves ions within a single gate zone by 110 μm for single-qubit addressing. c, Times for qubit operations, transport and cooling. Circuits can be run using two different measurement protocols. For circuits in which all measurements are made at the end, we use the high-fidelity measurement setting. Circuits containing mid-circuit measurements use shorter-duration measurements to minimize the crosstalk error on idle qubits. The shorter detection time measurement error is ~7 × 10−3, about twice as large as those reported in Table 1. Mid-circuit measurements induce an error of ~1% on neighbouring idle qubits, as measured by a Ramsey experiment. There are three different cooling stages used during transport (stages 1 and 2) and before gates (stage 3) and are either implemented through Doppler or sideband (SB) cooling. d, Construction of a phase-insensitive TQ gate. The Mølmer–Sørensen interaction generates the unitary \({U}_{{\rm{MS}}}=\exp [-{\rm{i}}\frac{{\rm{\pi }}}{4}{(X\sin \varphi +Y\cos \varphi )}^{\otimes 2}]\) (orange), whose basis is determined by the optical phase ϕ. SQ operations driven by the same laser beams generate the unitary \({U}_{{\rm{SQ}}}=\exp [-{\rm{i}}\frac{{\rm{\pi }}}{4}(X\cos \,\varphi +Y\sin \,\varphi )]\) (blue) and are applied globally to both qubits. The resulting composite gate is, up to a global phase, given by \({U}_{zz}=\exp (-{\rm{i}}\frac{{\rm{\pi }}}{4}Z\otimes Z)\) (green).

Extended Data Fig. 2 Estimated gate errors and randomized benchmarking results.

a, SQ RB for all four qubits. b, TQ RB results for zone 1. c, TQ RB results for zone 2. d, An estimated error budget for a single TQ gate operation. e, Correlation parameters for simultaneous TQ RB. Each square represents a value of δi,j with the numbers being scaled by 10−4. All are within one standard deviation of zero. f, Addressability error estimates for simultaneous RB. The quantities \({\alpha }_{z}^{{\rm{both}}}\) and γz refer to simultaneous RB decay rates and their deviation from individual RB decay rates, as described in the text.

Extended Data Fig. 3 Teleported CNOT gate experiment results.

a, Circuit implementing a teleported CNOT gate, with q0 and q3 the control and target qubit, respectively. b, Bar plots showing the distribution of measurement outcomes when qubits q0 and q3 are prepared and measured in the {|0, |1} and {|+, |−} bases. c, The teleported CNOT gate time budget.

Extended Data Fig. 4 QV N = 4 results.

a, Example of the transport operations needed for part of an N = 4 QV circuit. Trap regions are labelled as load zone (L); auxiliary zone 1 (A1); gate zone 1, left (G1L); gate zone 1, centre (G1C); gate zone 1, right (G1R); auxiliary zone 2 (A2); gate zone 2, left (G2L); gate zone 2, centre (G2C); and gate zone 2, right (G2R). b, An N = 4 QV circuit time budget.

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Pino, J.M., Dreiling, J.M., Figgatt, C. et al. Demonstration of the trapped-ion quantum CCD computer architecture. Nature 592, 209–213 (2021). https://doi.org/10.1038/s41586-021-03318-4

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