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

TL;DR

This paper demonstrates a quantum convolutional neural network implemented on a superconducting quantum processor that effectively recognizes topological phases in quantum states, outperforming direct measurement methods in fidelity.

Contribution

The authors realize and benchmark a quantum convolutional neural network on a 7-qubit superconducting processor for identifying quantum topological phases, showing improved recognition fidelity.

Findings

QCNN outperforms direct measurements in recognizing topological phases.

Finite-fidelity gates still enable effective phase recognition.

Hardware-efficient, low-depth circuits successfully prepare relevant quantum states.

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.