C. B. Osborn

Unsupervised Machine Learning on a Hybrid Quantum Computer

  1. J. S. Otterbach,
  2. R. Manenti,
  3. N. Alidoust,
  4. A. Bestwick,
  5. M. Block,
  6. B. Bloom,
  7. S. Caldwell,
  8. N. Didier,
  9. E. Schuyler Fried,
  10. S. Hong,
  11. P. Karalekas,
  12. C. B. Osborn,
  13. A. Papageorge,
  14. E. C. Peterson,
  15. G. Prawiroatmodjo,
  16. N. Rubin,
  17. Colm A. Ryan,
  18. D. Scarabelli,
  19. M. Scheer,
  20. E. A. Sete,
  21. P. Sivarajah,
  22. Robert S. Smith,
  23. A. Staley,
  24. N. Tezak,
  25. W. J. Zeng,
  26. A. Hudson,
  27. Blake R. Johnson,
  28. M. Reagor,
  29. M. P. da Silva,
  30. and C. Rigetti
Machine learning techniques have led to broad adoption of a statistical model of computing. The statistical distributions natively available on quantum processors are a superset of
those available classically. Harnessing this attribute has the potential to accelerate or otherwise improve machine learning relative to purely classical performance. A key challenge toward that goal is learning to hybridize classical computing resources and traditional learning techniques with the emerging capabilities of general purpose quantum processors. Here, we demonstrate such hybridization by training a 19-qubit gate model processor to solve a clustering problem, a foundational challenge in unsupervised learning. We use the quantum approximate optimization algorithm in conjunction with a gradient-free Bayesian optimization to train the quantum machine. This quantum/classical hybrid algorithm shows robustness to realistic noise, and we find evidence that classical optimization can be used to train around both coherent and incoherent imperfections.
arxiv pdf

Demonstration of Universal Parametric Entangling Gates on a Multi-Qubit Lattice

  1. M. Reagor,
  2. C. B. Osborn,
  3. N. Tezak,
  4. A. Staley,
  5. G. Prawiroatmodjo,
  6. M. Scheer,
  7. N. Alidoust,
  8. E. A. Sete,
  9. N. Didier,
  10. M. P. da Silva,
  11. E. Acala,
  12. J. Angeles,
  13. A. Bestwick,
  14. M. Block,
  15. B. Bloom,
  16. A. Bradley,
  17. C. Bui,
  18. S. Caldwell,
  19. L. Capelluto,
  20. R. Chilcott,
  21. J. Cordova,
  22. G. Crossman,
  23. M. Curtis,
  24. S. Deshpande,
  25. T. El Bouayadi,
  26. D. Girshovich,
  27. S. Hong,
  28. A. Hudson,
  29. P. Karalekas,
  30. K. Kuang,
  31. M. Lenihan,
  32. R. Manenti,
  33. T. Manning,
  34. J. Marshall,
  35. Y. Mohan,
  36. W. O'Brien,
  37. J. Otterbach,
  38. A. Papageorge,
  39. J.-P. Paquette,
  40. M. Pelstring,
  41. A. Polloreno,
  42. V. Rawat,
  43. C. A. Ryan,
  44. R. Renzas,
  45. N. Rubin,
  46. D. Russell,
  47. M. Rust,
  48. D. Scarabelli,
  49. M. Selvanayagam,
  50. R. Sinclair,
  51. R. Smith,
  52. M. Suska,
  53. T.-W. To,
  54. M. Vahidpour,
  55. N. Vodrahalli,
  56. T. Whyland,
  57. K. Yadav,
  58. W. Zeng,
  59. and C. T. Rigetti
We show that parametric coupling techniques can be used to generate selective entangling interactions for multi-qubit processors. By inducing coherent population exchange between adjacent
qubits under frequency modulation, we implement a universal gateset for a linear array of four superconducting qubits. An average process fidelity of =93% is measured by benchmarking three two-qubit gates with quantum process tomography. In order to test the suitability of these techniques for larger computations, we prepare a six-qubit register in all possible bitstring permutations and monitor the performance of a two-qubit gate on another pair of qubits. Across all these experiments, an average fidelity of =91.6±2.6% is observed. These results thus offer a path to a scalable architecture with high selectivity and low crosstalk.
arxiv pdf