Realizing quantum convolutional neural networks on a superconducting quantum processor to recognize quantum phases.
Johannes Herrmann, Sergi Masot Llima, Ants Remm, Petr Zapletal, Nathan A McMahon, Colin Scarato, François Swiadek, Christian Kraglund Andersen, Christoph Hellings, Sebastian Krinner, Nathan Lacroix, Stefania Lazar, Michael Kerschbaum, Dante Colao Zanuz, Graham J Norris, Michael J Hartmann, Andreas Wallraff, Christopher Eichler
Author Information
Johannes Herrmann: Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland. johannes.herrmann@phys.ethz.ch.
Sergi Masot Llima: Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland.
Ants Remm: Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland.
Petr Zapletal: Department of Physics, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany. ORCID
Nathan A McMahon: Department of Physics, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany.
Colin Scarato: Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland. ORCID
François Swiadek: Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland.
Christian Kraglund Andersen: Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland. ORCID
Christoph Hellings: Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland. ORCID
Sebastian Krinner: Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland.
Nathan Lacroix: Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland. ORCID
Stefania Lazar: Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland.
Michael Kerschbaum: Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland.
Dante Colao Zanuz: Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland.
Graham J Norris: Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland.
Michael J Hartmann: Department of Physics, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany. ORCID
Andreas Wallraff: Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland. ORCID
Christopher Eichler: Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland. eichlerc@phys.ethz.ch. ORCID
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.
References
Phys Rev Lett. 2019 May 31;122(21):210503
[PMID: 31283312]
Nat Commun. 2020 Feb 10;11(1):808
[PMID: 32041956]
W911NF-16-1-0071/ODNI | Intelligence Advanced Research Projects Activity (IARPA)
206021-170731/Swiss National Science Foundation | National Center of Competence in Research Quantum Science and Technology (NCCR "QSIT - Quantum Science and Technology")
