Beyond Binary Quantum Information
Mehul recently took part in STEM@Helix at the Falkirk Science Festival where he spoke about quantum entanglement with the help of some coloured blocks and two lab snacks boxes. It was harder than expected, but the audience had some particularly insightful questions about the quantum world and science careers!
We recently celebrated five fantastic years of BBQLab with a proper international BBQLab BBQ and a five-dimensional cake!
We recently attended the very exciting CLEO 2023 conference in San Jose, CA. The conference included a fascinating range of talks from academia and industry on topics as diverse as deep neural networks, multi-mode fibres, and critical coupling. Mehul presented an invited talk on “harnessing complexity for manipulating spatiotemporal entanglement” at the Symposium on Enabling Highly Multimode Nonlinear and Quantum Photonics, organised by Logan Wright and Marco Piccardo. He also presented a talk on our work on our work on noise and loss-robust quantum steering in the Quantum Network Protocols session. Besides all the great science, it was also very nice to catch up with old friends and colleagues from around the world!
We are excited to announce that our latest work ‘Simultaneously Sorting Overlapping Quantum States of Light‘ has been published in Physical Review Letters. In this collaboration with the QOCI Group, we demonstrate simultaneous and efficient sorting of non-orthogonal transverse-spatial states of light in up to seven dimensions. This has been made possible by employing a multi-plane light converter (MPLC) to program high-dimensional POVMs that correspond to unambiguous discrimination of the quantum states. The MPLC employs an additional auxiliary outcome that sorts the overlap of all the modes into an ambiguous outcome.
An implication of this method is that we can sort overlapping images encoded with coherent sources. We demonstrate this by sorting three smiley faces with an accuracy of 97.6%, implying accurate image classification with light!
In our new preprint titled Referenceless characterisation of complex media using physics-informed neural networks, we show how multi-plane neural networks (MPNN) can be used to recover the complex transmission matrix of a commercial multi-mode fibre in a noise-robust manner, without using a reference field! We also show how the MPNN technique can be used to characterise a series of independent complex media, as shown in the figure above. This work will have many applications ranging from classical optical networks, biomedical imaging, to quantum information processing. As just one example, the MPNN technique forms a central part of our previous work on programming high-dimensional quantum gates inside a multi-mode fibre using inverse-design.
BBQ Lab 