Stefano Mensa, Emre Sahin, et al.
Machine Learning: Science and Tech.
The ability to generate and verify multipartite entanglement is an important benchmark for near-term quantum devices. We develop a scalable entanglement metric based on multiple quantum coherences and demonstrate experimentally on a 20-qubit superconducting device. We report a state fidelity of 0.5165±0.0036 for an 18-qubit GHZ state, indicating multipartite entanglement across all 18 qubits. Our entanglement metric is robust to noise and only requires measuring the population in the ground state; it can be readily applied to other quantum devices to verify multipartite entanglement.
Stefano Mensa, Emre Sahin, et al.
Machine Learning: Science and Tech.
Abhinav Kandala
APS March Meeting 2022
Anupam Madhukar, Jiefei Zhang, et al.
MRS Spring/Fall Meeting 2020
David McKay, Lev Bishop, et al.
APS March Meeting 2020