2D polaritons for optoelectronic devices and networks
Lead Research Organisation:
University of Sheffield
Department Name: School of Mathematics and Physical Sciences
Abstract
The rate of information growth corresponds to an annual increase of 19%, reaching 100 zettabytes by the end of 2022, and novel optoelectronic tools are required for fast information processing. With perpetual generation and flow of information around, increasing the bit rates of devices that process information is imperative for sustainable future. Typically, optical signals - photons - are sent over fibre links, and that is how majority of internet traffic flows. However, photons do not interact with each other, unless they couple to a medium in which they propagate. One way to act is converting light into electronic signals, and processing signals with conventional electronics. However, in this case Ohmic losses reduce energy efficiency and processing speed is defined by electronic timescales. A distinct way to process light relies on strong light-matter coupling. When photons are coupled strongly to optical transitions and particles in semiconductors, they become hybrid light-matter particles - polaritons. Polaritons acquire nonlinearity and allow for information processing in an all-optical way. The efficiency of this process largely depends on many-body properties on materials used for building optical devices.
The project aims to develop a distinct family of optoelectronic devices by exploiting many-body interactions in semiconducting bilayers. Recent results show a highly nonlinear polaritonic response in systems of transition metal dichalcogenides (TMDCs) when these 2D materials are doped with excessive charge (for instance, free electrons). In bilayer geometry, they reveal a zoo of various intralayer and interlayer quasiparticles based on bound electron-hole pairs correlated with electrons. By coupling these quasiparticles to light, we expect that strong coupling merged with many-body interactions will lead to game-changing increase of polaritonic nonlinearity. However, accessing this physics requires developing new theoretical tools that can capture strong correlations in such a system. Many other properties needed for building polaritonic circuits and processing units are yet to be explored.
In the project, we aim to develop a theoretical description of 2D polaritons in transition metal dichalcogenides and propose blueprints for optoelectronic devices that use polaritonic many-body interactions. Our project is structured around three objectives.
1. We will develop a theoretical description of nonlinear response in doped TMDC bilayers in order to characterise many-body interactions of 2D polaritons.
2. We will study nontrivial transport properties of doped TMDC bilayers to design polaritonic circuits based on many-body interactions.
3. We will use highly nonlinear polaritonic lattices in TMDC heterobilayers to develop polaritonic computational networks.
As a result, we will develop the background for future 2D polaritonic devices based on highly nonlinear bilayer systems.
The project aims to develop a distinct family of optoelectronic devices by exploiting many-body interactions in semiconducting bilayers. Recent results show a highly nonlinear polaritonic response in systems of transition metal dichalcogenides (TMDCs) when these 2D materials are doped with excessive charge (for instance, free electrons). In bilayer geometry, they reveal a zoo of various intralayer and interlayer quasiparticles based on bound electron-hole pairs correlated with electrons. By coupling these quasiparticles to light, we expect that strong coupling merged with many-body interactions will lead to game-changing increase of polaritonic nonlinearity. However, accessing this physics requires developing new theoretical tools that can capture strong correlations in such a system. Many other properties needed for building polaritonic circuits and processing units are yet to be explored.
In the project, we aim to develop a theoretical description of 2D polaritons in transition metal dichalcogenides and propose blueprints for optoelectronic devices that use polaritonic many-body interactions. Our project is structured around three objectives.
1. We will develop a theoretical description of nonlinear response in doped TMDC bilayers in order to characterise many-body interactions of 2D polaritons.
2. We will study nontrivial transport properties of doped TMDC bilayers to design polaritonic circuits based on many-body interactions.
3. We will use highly nonlinear polaritonic lattices in TMDC heterobilayers to develop polaritonic computational networks.
As a result, we will develop the background for future 2D polaritonic devices based on highly nonlinear bilayer systems.
People |
ORCID iD |
| Oleksandr Kyriienko (Principal Investigator) |
Publications
Adl H
(2025)
Tunable Room-Temperature Polaritons in the Very Strong Coupling Regime in Quasi-2D Ruddlesden-Popper Perovskites
in Advanced Optical Materials
Benimetskiy F. A.
(2025)
All-optical nonlinear phase modulation in open semiconductor microcavities
Genco A
(2025)
Femtosecond switching of strong light-matter interactions in microcavities with two-dimensional semiconductors
in Nature Communications
Song K
(2025)
Electrically Tunable and Enhanced Nonlinearity of Moiré Exciton Polaritons in Transition Metal Dichalcogenide Bilayers
in Physical Review Letters
Struve M
(2026)
Room-temperature polariton condensate in a quasi-2D hybrid perovskite.
in Nature communications
Tsiamis I
(2026)
Continuous-wave quantum light control via engineered Rydberg-induced dephasing
in Physical Review A
Tsiamis I
(2026)
Continuous-wave all-optical single-photon transistor based on a Rydberg-atom ensemble
in Physical Review A
Wang Y.
(2025)
Photonics-Enhanced Graph Convolutional Networks
Wang Y.
(2025)
Polaritonic Machine Learning for Graph-based Data Analysis
in arXiv
Related Projects
| Project Reference | Relationship | Related To | Start | End | Award Value |
|---|---|---|---|---|---|
| EP/X017222/1 | 01/03/2023 | 31/12/2024 | £202,250 | ||
| EP/X017222/2 | Transfer | EP/X017222/1 | 01/01/2025 | 31/10/2025 | £43,815 |
| Description | This part of the project is just a continuation of the same project, moved from Exeter to Sheffield. After the first part was completed on studying interactions, we have concentrated on developing polaritonic machine learning and neuromorphic architectures. Specifically, we have developed a range of models for polaritonic machine learning approaches that are hybrid, hence merging classical AI and physics-based AI. This include approaches for graph learning based on polaritonic and optical systems, where we show advantage in using optical systems when doing protein classification or regression on their properties (like toxicity). This is important for drug discovery. We have also developed world-first generative AI model with polaritons, aiming for fast generation of images. |
| Exploitation Route | This follows up on the first part of the project, where models for interactions that we developed have been used by numerous groups to describe their systems at light-matter coupling, including groups in UK, Italy, Poland, Germany, France, Singapore, US. Our works on polaritonic machine learning have informed other groups and suggested new direction in terms of graph analysis. They led to new collaborations, grant applications, and experimental realisation of these models. |
| Sectors | Digital/Communication/Information Technologies (including Software) Education Electronics |
| Description | As of 2026, the findings from this project have been used to shape activity at the interface of polaritonic machine learning, neuromorphic devices, photonics, and quantum technologies. Building on earlier contributions to UK discussions on optical and quantum devices, this work now also informs my role as Director of the Sheffield Quantum Centre, where it underpins interdisciplinary events and discussions linking quantum technologies, materials science, and AI. The discussion of future of quantum and photonic technologies (https://sheffieldquantum.com/#debate) largely benefited from findings that we have. The project has also supported engagement with industry and the defence sector, contributed to a podcast on EdgeAI, and helped develop new collaborations with Cornell, CUNY, Heriot-Watt, Paris, and other partners. Together, these activities show how the findings are already helping to guide collaborations, strategic discussions, and emerging application directions. From the technical findings, polaritonic machine learning for protein analysis have the potential to improve drug discovery. |
| First Year Of Impact | 2025 |
| Sector | Digital/Communication/Information Technologies (including Software),Electronics,Pharmaceuticals and Medical Biotechnology |
| Description | Quantum Generative Modelling: from Foundations to Applications |
| Amount | £2,538,802 (GBP) |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 05/2026 |
| End | 05/2031 |
| Description | Collaboration with The City College of New York (CUNY), group of Prof. Vinod Menon |
| Organisation | City University of New York (CUNY) |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | We have started the collaboration during my recent trip to NY, discussing various approaches to polaritonic machine learning. I have proposed several ideas, in particular for preforming generated modelling that is improved by using physical systems. The work is now being completed and will be submitted soon. |
| Collaborator Contribution | The collaborators at CUNY contributed know-how in polaritonic experiments and did measurement for polaritonic GenAI. |
| Impact | We have now prepared one manuscript (to be submitted) on polaritonic generative AI (theory + experiment), and discuss new ideas in the area. |
| Start Year | 2025 |
| Description | Partnership with Oxford Quantum Circuits on quantum graph learning |
| Organisation | Oxford Quantum Circuits |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Over the last year, I have engaged in discussions with Oxford Quantum Circuits (OQC), with the collaboration becoming more structured in 2025. The key to pushing collaboration further was the Quantum Use-Case Sandpit at the Sheffield Quantum Centre that I have organised. In terms of research, my contribution focused on shaping research directions for hybrid quantum-classical pipelines, particularly where classical machine learning can be supplemented by quantum hardware. Through joint brainstorming and project development, we have now defined a shared research direction and are about to begin work on this project. |
| Collaborator Contribution | Our partners at Oxford Quantum Circuits contributed by identifying promising application areas and helping shape the research direction around them. They provided guidance on device capabilities and practical hardware considerations, which was important for determining a realistic and effective project architecture. They also worked closely with us in joint brainstorming and project development, and are contributing support for a postdoctoral researcher for six months to carry out the pilot project. |
| Impact | This is a developing collaboration with interdisciplinary nature, merging physics (hardware), computer science (machine learning), and quantum information approaches. We have the ideas ready, and will see results after the pilot project soon. |
| Start Year | 2025 |