Evidence map›Paper›PMID 42236720›Full record

ArticleNature communications2026

Photonic Mixture-of-Experts for scalable multi-task on-chip optical neural networks.

Wencan Liu, Zhenghang Zhang, Peng Meng Chan, Run Sun, Yutong He, Yuhao Wang, Caihua Zhang, Sigang Yang, Tingzhao Fu, Yuyao Huang and 2 more

Abstract read
PubMed Full text at PMC Publisher
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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Wencan LiuDepartment of Electronic Engineering, Tsinghua University, Beijing, China.ORCID http://orcid.org/0009-0009-8937-6293
Zhenghang ZhangWeixian College, Tsinghua University, Beijing, China.ORCID http://orcid.org/0009-0007-0165-6537
Peng Meng ChanDepartment of Electronic Engineering, Tsinghua University, Beijing, China.ORCID http://orcid.org/0009-0000-2194-8448
Run SunDepartment of Electronic Engineering, Tsinghua University, Beijing, China.
Yutong HeDepartment of Electronic Engineering, Tsinghua University, Beijing, China.
Yuhao WangDepartment of Electronic Engineering, Tsinghua University, Beijing, China.
Caihua ZhangDepartment of Electronic Engineering, Tsinghua University, Beijing, China.
Sigang YangDepartment of Electronic Engineering, Tsinghua University, Beijing, China.
Tingzhao FuCollege of Advanced Interdisciplinary Studies & Hunan Provincial Key Laboratory of Novel Nano-Optoelectronic Information Materials and Devices, National University of Defense Technology, Changsha, Hunan, China. futingzhao@nudt.edu.cn.ORCID http://orcid.org/0009-0003-0917-9721
Yuyao HuangDepartment of Electronic Engineering, Tsinghua University, Beijing, China. huang-yy21@tsinghua.org.cn.ORCID http://orcid.org/0000-0002-3989-9514
Chaoran HuangDepartment of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.ORCID http://orcid.org/0000-0001-6997-758X
Hongwei ChenDepartment of Electronic Engineering, Tsinghua University, Beijing, China. chenhw@tsinghua.edu.cn.ORCID http://orcid.org/0000-0002-2952-2203

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62135009National Natural Science Foundation of China (National Science Foundation of China) 62405370
6 · The paper itself

Abstract

Photonic computing offers an energy-efficient, high-bandwidth platform for artificial intelligence (AI) but currently faces scalability bottlenecks stemming from depth-dependent designs, linear optical structures, and intrinsic optical losses, along with high hardware and reconfiguration costs for multi-task processing. Here, we present a scaling paradigm that circumvents these limitations by expanding network width rather than depth, leveraging the intrinsic parallelism of photonics. We implement a scalable Photonic Mixture-of-Experts (PMoE) architecture, where parallel photonic cores function as expert networks. By dynamically routing inputs to these experts, the PMoE efficiently executes multi-task workloads without altering the physical optical weights. We fabricated a PMoE chip integrating three collaborative diffraction-based expert networks, featuring 18 parallel kernels within a compact intrinsic computational-core footprint of 0.067 mm

Identifiers

PMID42236720
PMCPMC13396494

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.