Scalable Single-Step Generation of W States in 2D Superconducting Qubit Lattices
Jo\~ao H. Romeiro, Federico A. Roy, Niklas Bruckmoser, Ivan Tsitsilin, Niklas J. Glaser, Christian M. F. Schneider, Gerhard B. P. Huber, Saya A. Sch\"obe, Johannes Schirk, Florian Wallner, Malay Singh, Julius Feigl, Leon Koch, Lasse S\"odergren, Max Werninghaus, Stefan Filipp

TL;DR
This paper presents a fast, scalable method for generating W states in 2D superconducting qubit lattices using simultaneous interactions, significantly reducing entanglement times compared to sequential gate approaches.
Contribution
The authors introduce a novel protocol for directly creating large W states in 2D superconducting qubit lattices through engineered simultaneous interactions, enabling rapid entanglement generation.
Findings
Generated a 6-qubit W state in 99 ns with 83.9% fidelity.
Extended protocol to create 7-qubit W states within 264 ns.
Demonstrated scalability with entanglement time scaling with the largest lattice dimension.
Abstract
The reliable generation of multi-qubit entanglement is a prerequisite for large-scale quantum information technologies. In particular, W states are a valuable resource owing to their resilience under local loss or measurement. Nevertheless, preparing these states with sequential two-qubit gates often requires substantial time overhead. By contrast, engineered simultaneous interactions enable fast entanglement generation, even in qubit systems with limited nearest-neighbour connectivity. Here, we demonstrate a set of fast and robust operations for coherently distributing a single excitation across a lattice of arbitrary size, thereby directly generating W states from initial product states. In 2D lattices, the excitation propagates along both directions simultaneously, such that the total entanglement time scales only with the largest dimension. We exploit this property to prepare a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
