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

Understand

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.