Quantum nonlinearity for optical neural computing.

Qingyi Zhou, Jungmin Kim, Yutian Tao, Guoming Huang, Ming Zhou, Zewei Shao, Zongfu Yu

Abstract read

In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Qingyi Zhou *Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA. qzhou75@wisc.edu.ORCID http://orcid.org/0000-0001-7261-1060

Jungmin Kim *Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA.ORCID http://orcid.org/0000-0001-8567-5338

Yutian Tao *The Computer Sciences Department, University of Wisconsin-Madison, Madison, WI, USA.ORCID http://orcid.org/0009-0009-1664-5886

Guoming HuangDepartment of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA.

Ming ZhouDepartment of Electrical Engineering, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-2368-8883

Zewei ShaoDepartment of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA.ORCID http://orcid.org/0000-0002-8748-0638

Zongfu YuDepartment of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA.ORCID http://orcid.org/0000-0002-4536-1526

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid scaling of deep neural networks comes at the cost of unsustainable power consumption. While optical neural networks offer an alternative, their capabilities remain constrained by the lack of efficient optical nonlinearities. To address this, we propose an optical neural computing architecture by embedding quantum emitters in inverse-designed nanophotonic structures. Due to their saturability, quantum emitters exhibit exceptionally strong nonlinearity compared with conventional materials. Using physics-aware training, we numerically demonstrate that the proposed architecture can solve complex tasks, including nonlinear classification and reinforcement learning, within all-optical neural networks. To enable fair comparison across different platforms, we introduce a framework that quantitatively links nonlinearity to a network's expressive power. Analysis shows that our quantum activation operates at nW/μm

Identifiers

PMID42802161

DOI10.1038/s41467-026-77202-y

PMCPMC13616904

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.