Realizing quantum convolutional neural networks on a superconducting quantum processor to recognize quantum phases
OPEN ACCESS
Loading...
Author / Producer
Date
2022
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
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.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
13
Pages / Article No.
4144
Publisher
Nature
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03720 - Wallraff, Andreas / Wallraff, Andreas
Notes
Funding
820363 - An Open Superconducting Quantum Computer (EC)
170731 - 10 Millikelvin Cryostat for Quantum Science with Tens to Hundreds of Superconducting Qubits (SNF)
170731 - 10 Millikelvin Cryostat for Quantum Science with Tens to Hundreds of Superconducting Qubits (SNF)