A manufacturable platform for photonic quantum computing

Although holding great promise for low noise, ease of operation and networking<sup>1</sup>, useful photonic quantum computing has been precluded by the need for beyond-state-of-the-art components, manufactured by the millions<sup>2-6</sup>. Here we introduce a manufacturable platform<sup>7</sup> for quantum computing with photons. We benchmark a set of monolithically integrated silicon-photonics-based modules to generate, manipulate, network and detect heralded photonic qubits, demonstrating dual-rail photonic qubits with 99.98% ± 0.01% state preparation and measurement fidelity, Hong-Ou-Mandel (HOM) quantum interference between independent photon sources with 99.50% ± 0.25% visibility, two-qubit fusion with 99.22% ± 0.12% fidelity and a chip-to-chip qubit interconnect with 99.72% ± 0.04% fidelity, conditional on photon detection and not accounting for loss. We preview a selection of next-generation technologies: low-loss silicon nitride (SiN) waveguides and components to address loss, as well as fabrication-tolerant photon sources, high-efficiency photon-number-resolving detectors (PNRDs), low-loss chip-to-fibre coupling and barium titanate (BTO) electro-optic phase shifters for high-performance fast switching.

Authors

Institutions

Publication Details

Journal
Nature
Published
2025-02-26
DOI
https://doi.org/10.1038/s41586-025-08820-7
Citations
131
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
325.01
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A manufacturable platform for photonic quantum computing

Peter O. Weigel, Chia‐Ming Chang, Davide Tamborini, François-Xavier Musalem et al.
131 citations
Nature
Neural Networks and Reservoir Computing
325.01
article

A manufacturable platform for photonic quantum computing

Peter O. Weigel, Chia‐Ming Chang, Davide Tamborini, François-Xavier Musalem, Avishai Benyamini, J. Elliott Ortmann, Geoff Campbell, Chris Sparrow, Patrick S. Goley, Matthew House, Hong Hu, Nicholas Penthorn, Ben M. Burridge, Eric Dudley, Mihai D. Vidrighin, Brennan Peterson, Jason Horng, Nicholas LiCausi, Hugo Cable, J. H. D. Munns, Colleen S. Fenrich, Gabriel Catalano, Ryan Haislmaier, T. Hugh Jones, Mohsen Jadidi, Andrzej Veitia, Geoff J. Pryde, Jonathan Frazer, Dan Llewellyn, Sami I. Halimi, Jake Smith, Camille Stavrakas, Dylan S. Black, Vijay Sukumaran, Masaya Fukami, George Kovall, Hyungki Shin, V.K. Jain, Mercedes Gimeno-Segovia, Vitor R. Manfrinato, Fariba Danesh, Peter Krogen, Brian J. Roxworthy, Koen Alexander, Alexey Vert, Hsuan-Tung Peng, Jamie P. Wooding, Mark G. Thompson, Gyeongho Son, Paul Hansen, Michael W. Davis, Faraz Najafi, Damien Bonneau, Nikhil Kumar, Nicholas Kelez, Gabriel Omar Mendoza Conde, Koustuban Ravi, G. A. Peterson, Brad Moores, Matteo Staffaroni, Yogeeswaran Ganesan, Mário C. M. M. Souza, I. Vorobeichik, Deesha Shah, Khanh Bao Tran, Kimberly Lokovic, Terry Rudolph, J.H. Sinsky, D. J. Saunders, Shaunak Mukherjee, Sunil Pai, Mark Triplett, S. Choudhury, Tom Dauer, Ping Er-Xuan, Josep Fargas, Sebastian Goeldi, Matt Poush, Tarun Ramprasad, Maryann Tung, Sam Hardy, Angelita Viejo Rodriguez, Young-Ik Sohn, Gary A. P. Gibson, Xinran Zhou, Henrik Johansson, Ann Melnichuk, Jeremy L. O’Brien, Alessandro Farsi, Yong Liang, Pete Shadbolt, Michael Lovelady, Bryan Park, Vimal Kamineni, Stanley P. Burgos, Alejandro Ceballos, Andrea Bahgat Shehata, Gareth Ray, Ben Sohn
article en
131 citations

Abstract

Although holding great promise for low noise, ease of operation and networking<sup>1</sup>, useful photonic quantum computing has been precluded by the need for beyond-state-of-the-art components, manufactured by the millions<sup>2-6</sup>. Here we introduce a manufacturable platform<sup>7</sup> for quantum computing with photons. We benchmark a set of monolithically integrated silicon-photonics-based modules to generate, manipulate, network and detect heralded photonic qubits, demonstrating dual-rail photonic qubits with 99.98% ± 0.01% state preparation and measurement fidelity, Hong-Ou-Mandel (HOM) quantum interference between independent photon sources with 99.50% ± 0.25% visibility, two-qubit fusion with 99.22% ± 0.12% fidelity and a chip-to-chip qubit interconnect with 99.72% ± 0.04% fidelity, conditional on photon detection and not accounting for loss. We preview a selection of next-generation technologies: low-loss silicon nitride (SiN) waveguides and components to address loss, as well as fabrication-tolerant photon sources, high-efficiency photon-number-resolving detectors (PNRDs), low-loss chip-to-fibre coupling and barium titanate (BTO) electro-optic phase shifters for high-performance fast switching.

NatureVol. 641(8064)
Sci-Tech Daresbury (GB), Praevium Research (United States) (US)
Openalex Percentile: Top 0%
Neural Networks and Reservoir Computing
325.01
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.