Realizing Quantum Convolutional Neural Networks on a Superconducting
Quantum Processor to Recognize Quantum Phases
- URL: http://arxiv.org/abs/2109.05909v1
- Date: Mon, 13 Sep 2021 12:32:57 GMT
- Title: Realizing Quantum Convolutional Neural Networks on a Superconducting
Quantum Processor to Recognize Quantum Phases
- Authors: Johannes Herrmann, Sergi Masot Llima, Ants Remm, Petr Zapletal, Nathan
A. McMahon, Colin Scarato, Francois 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
- Abstract summary: 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.
We realize a quantum convolutional neural network (QCNN) on a 7-qubit superconducting quantum processor to identify symmetry-protected topological phases of a spin model characterized by a non-zero string order parameter.
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.
- Score: 2.1465372441653354
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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