Projects
I’ve been fortunate enough to work across a number of fields in applied physics and neuroscience. If there is any theme, it is probably light and how it can enable us to understand the limits of computation. From wet, noisy computers (e.g. mammalian brains) to low-noise, silicon-based computers (e.g. quantum computers). Below is a somewhat coherent set of highlights, but my full list of publications can be found here.
Neuroscience
After working on programmable and quantum photonic systems for a decade, I pivoted into neuroscience to apply optical control and measurement to the study of living neural circuits. At UCL, I developed an all-optical approach combining optogenetics and high-speed two-photon voltage imaging in the awake brain to probe synaptic plasticity in the cerebellum:
All-optical voltage interrogation for probing synaptic plasticity in vivo
J. Carolan et al., Nature Communications, (2025).
Programmable Photonics
My PhD work developed the first universal linear optical processor: a single photonic chip that can be programmed to implement any possible linear optical operation. We used it to perform years’ worth of quantum-optical experiments in a few weeks. Over the years, along with my colleagues, we have used these systems to develop and implement new algorithms in quantum and classical optical computing:
- Universal linear optics J. Carolan et al., Science (2015).
- Variational quantum unsampling on a quantum photonic processor J. Carolan et al., Nature Physics (2020).
- On the experimental verification of quantum complexity in linear optics J. Carolan et al., Nature Photonics (2014).
- Simulating the vibrational quantum dynamics of molecules using photonics C. Sparrow et al., Nature (2018).
Quantum Photonics
To realise the full potential of photonic quantum technologies you need to find a way to make photons interact (nonlinearities). I’ve worked on this both from a theoretical perspective where, along with my colleagues, we invented the Quantum Optical Neural Network, and experimentally, by engineering silicon photonics and quantum dot systems.
- Quantum optical neural networks G. Steinbrecher… J. Carolan, npj Quantum Information (2019).
- Scalable feedback control of single photon sources for photonic quantum technologies J. Carolan et al., Optica, (2019).
- Dynamical photon–photon interaction mediated by a quantum emitter H. Le Jeannic et al., Nature Physics (2022).
- Ultra-low loss quantum photonic circuits integrated with single quantum emitters A. Chanana et al., Nature Communications (2022).
Reviews
- Implanted cortical neuroprosthetics for speech and movement restoration W. Muirhead et al., Journal of Neurology, (2024).
- Linear programmable nanophotonic processors N. Harris et al., Optica (2018).
- Hybrid integration methods for on-chip quantum photonics J. H. Kim et al., Optica (2020).
- Quantum-dot-based deterministic photon–emitter interfaces for scalable photonic quantum technology R. Uppu et al., Nature Nanotechnology (2021).
Philosophy
In a former life I studied Philosophy. Recently, in collaboration with some great friends — Karim Thébault and Dominik Hangleiter — we gave a philosophical treatment to the emerging field of analogue quantum simulation in a book published by Springer in 2022. This project is the culmination of a chance meeting, unlimited enthusiasm and many, many Skype meetings. I’m deeply grateful to my collaborators for allowing an annoying experimentalist to join for the ride.
D. Hangleiter, J. Carolan and K. Thébault, Analogue Quantum Simulation: A New Instrument for Scientific Understanding (Springer, 2022).
Patents
- Scalable integration of hybrid optoelectronic and quantum optical systems into photonic circuits US11054590B1
- Quantum optical neural networks US11790221B2
- Scalable feedback control of single photon sources for photonic quantum technologies US11237454B2
- Apparatus and methods for optical neural network US10268232B2