Realizing Quantum Convolutional Neural Networks on a Superconducting Quantum Processor to Recognize Quantum Phases

  1. Johannes Herrmann,
  2. Sergi Masot Llima,
  3. Ants Remm,
  4. Petr Zapletal,
  5. Nathan A. McMahon,
  6. Colin Scarato,
  7. Francois Swiadek,
  8. Christian Kraglund Andersen,
  9. Christoph Hellings,
  10. Sebastian Krinner,
  11. Nathan Lacroix,
  12. Stefania Lazar,
  13. Michael Kerschbaum,
  14. Dante Colao Zanuz,
  15. Graham J. Norris,
  16. Michael J. Hartmann,
  17. Andreas Wallraff,
  18. and Christopher Eichler
Quantum computing crucially relies on the ability to efficiently characterize the quantum states output by quantum hardware. Conventional methods which probe these states through direct
measurements and classically computed correlations become computationally expensive when increasing the system size. Quantum neural networks tailored to recognize specific features of quantum states by combining unitary operations, measurements and feedforward promise to require fewer measurements and to tolerate errors. Here, we realize a quantum convolutional neural network (QCNN) on a 7-qubit superconducting quantum processor to identify symmetry-protected topological (SPT) phases of a spin model characterized by a non-zero string order parameter. We benchmark the performance of the QCNN based on approximate ground states of a family of cluster-Ising Hamiltonians which we prepare using a hardware-efficient, low-depth state preparation circuit. We find that, despite being composed of finite-fidelity gates itself, the QCNN recognizes the topological phase with higher fidelity than direct measurements of the string order parameter for the prepared states.

Implementation of Conditional-Phase Gates based on tunable ZZ-Interactions

  1. Michele C. Collodo,
  2. Johannes Herrmann,
  3. Nathan Lacroix,
  4. Christian Kraglund Andersen,
  5. Ants Remm,
  6. Stefania Lazar,
  7. Jean-Claude Besse,
  8. Theo Walter,
  9. Andreas Wallraff,
  10. and Christopher Eichler
High fidelity two-qubit gates exhibiting low crosstalk are essential building blocks for gate-based quantum information processing. In superconducting circuits two-qubit gates are typically
based either on RF-controlled interactions or on the in-situ tunability of qubit frequencies. Here, we present an alternative approach using a tunable cross-Kerr-type ZZ-interaction between two qubits, which we realize by a flux-tunable coupler element. We control the ZZ-coupling rate over three orders of magnitude to perform a rapid (38 ns), high-contrast, low leakage (0.14 %) conditional-phase CZ gate with a fidelity of 97.9 % without relying on the resonant interaction with a non-computational state. Furthermore, by exploiting the direct nature of the ZZ-coupling, we easily access the entire conditional-phase gate family by adjusting only a single control parameter.