8x8 Reconfigurable quantum photonic processor based on silicon nitride waveguides
Caterina Taballione, Tom A. W. Wolterink, Jasleen Lugani, Andreas, Eckstein, Bryn A. Bell, Robert Grootjans, Ilka Visscher, Dimitri Geskus,, Chris G. H. Roeloffzen, Jelmer J. Renema, Ian A. Walmsley, Pepijn W. H., Pinkse, Klaus-Jochen Boller

TL;DR
This paper presents a reconfigurable 8x8 silicon nitride photonic processor capable of implementing arbitrary linear transformations, validated through quantum interference experiments, marking a significant advancement in scalable integrated quantum photonics.
Contribution
The paper introduces the largest programmable silicon nitride photonic circuit to date, enabling complex quantum information processing primitives with high fidelity.
Findings
Successfully demonstrated arbitrary linear transformations.
Validated quantum primitives like Hong-Ou-Mandel interference.
Achieved high fidelities indicating scalability potential.
Abstract
The development of large-scale optical quantum information processing circuits ground on the stability and reconfigurability enabled by integrated photonics. We demonstrate a reconfigurable 8x8 integrated linear optical network based on silicon nitride waveguides for quantum information processing. Our processor implements a novel optical architecture enabling any arbitrary linear transformation and constitutes the largest programmable circuit reported so far on this platform. We validate a variety of photonic quantum information processing primitives, in the form of Hong-Ou-Mandel interference, bosonic coalescence/anticoalescence and high-dimensional single-photon quantum gates. We achieve fidelities that clearly demonstrate the promising future for large-scale photonic quantum information processing using low-loss silicon nitride.
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.
