BBQ Lab

We are happy to announce that our work has been published in AVS Quantum Science. In this special edition publication, we commemorate not only the important research of the late Jonathan P. Dowling, but also his notoriously wacky sense of humour. We demonstrate two-photon entanglement in the transverse spatial mode degree of freedom, particularly where one photon carries the fundamental Gaussian mode and the other a higher order Laguerre-Gaussian mode with index (ℓ) or radial (p). Taking inspiration from Dowling’s N00N states, we call these ℓ00ℓ-entangled states (we leave it up to your imagination as to what the radial (p) states may be called) .

Using the combination of a spatial light modulator (SLM) and a single mode fibre (SMF), we can make arbitrary coherent projective measurements on our generated ℓ00ℓ states. This allows us to demonstrate a “twisted” quantum eraser where Hong-Ou-Mandel interference is recovered between two distinguishable photons by projecting them into a rotated LG superposition basis. Furthermore, we verify entanglement by using an entanglement witness and reconstructing the density matrix.  We find that our generated ℓ00ℓ states have fidelities of 95.31% and 89.80% to their respective ideal maximally entangled states.

You can read the (open-access) publication here: https://pubs.aip.org/avs/aqs/article/5/4/045004/2920276/00-entanglement-and-the-twisted-quantum-eraser

Our work was recently published in Optics Express and was highlighted as the Editor’s Pick. In this work, we present a method to fully characterize the transmission matrices of complex media using neural networks.

While similar methods existed, few of them could measure the relative phases between rows of the transmission matrix. Relative phases are necessary for coherent control of light after it propagates through given complex media, allowing their applications in optical networks, biomedical imaging, and quantum information processing.

Doing simple modifications to our setup and performing randomised measurements allows full recovery of transmission matrix using (what we call) multi-plane neural networks (MPNN). We show that our technique performs a much more accurate measurement as compared to the standard existing method of measurements on the same physical setup. Moreover, our technique is extremely robust to noise, retrieving a high-quality transmission matrix even when the measured data is majorly just noise (upto SNR =0.8)!

We also demonstrate the scalability of this method, to characterize multiple complex media simultaneously in a highly non-trivial and non-convex system.

You can read more about this work which is open access at doi.org/10.1364/OE.500529 . We have included all the codes, experimental and simulational datasets with the paper which can be found on Zenodo.