Realizing quantum convolutional neural networks on a superconducting quantum processor to recognize quantum phases.

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 approxi

SUBMITTER: Herrmann J 

PROVIDER: S-EPMC9288436 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature