arXiv subjects

Hadrian Bezuidenhout

Publications and source records attributed to Hadrian Bezuidenhout.

4 recordsLinked to original sources

arXiv2026

Fractional optical skyrmions

Optical topologies in the form of Skyrmions have attracted significant interest of late, where their integer Skyrmion number has been shown to be robust to complex media. Here we create the first fractional Skyrmions by structuring light as a vectorial superposition of non-integer orbital angular momentum. We unravel the map structure to reveal a new phenomenon, the abrupt transition jumps in skyrmion number, which serves to reinforce the integer nature of skyrmion topologies. Our experimental demonstration agrees well with simulation, opening a new spectrum of optical topologies to explore, with exciting possibilities in optical communication and sensing.

physics.optics↗

arXiv2025

Deep diffractive optical neural networks for detecting Skyrmionic topologies of light

Optical Skyrmions are topological forms of structured light with the potential of an infinite encoding alphabet that is immune to disturbance. This attractive prospect is hindered by the lack of any topological detector, a challenging problem due to the non-orthogonal nature of the topological invariant (N). Here we demonstrate the first deterministic detector for Skyrmionic topologies of light using a deep diffractive optical neural network. Our network uses two independent processing channels of 5 diffractive layers each to map incoming topologies to spatially separated Gaussian channels from which N can be detected. We overcome the complexity of the training by using a spatial mode basis rather than pixels, reducing the training variables by x1000 compared to current methods. We use the detector on an input set of 81 input topologies, showing high accuracy even in the presence of significant levels of noise. Finally, to show the practical utility of the device, we transmit and receive an image encoded in a 14-level topological alphabet with no discernible cross-talk. Our work offers a new paradigm for the emergent field of diffractive optical networks and can easily be extended to other forms of optical topologies, setting a clear pathway for their deployment in real-world applications.

physics.optics↗

arXiv2024

Emulating quantum computing with optical matrix multiplication

Optical computing harnesses the speed of light to perform vector-matrix operations efficiently. It leverages interference, a cornerstone of quantum computing algorithms, to enable parallel computations. In this work, we interweave quantum computing with classical structured light by formulating the process of photonic matrix multiplication using quantum mechanical principles such as state superposition and subsequently demonstrate a well known algorithm, namely the Deutsch-Jozsa's algorithm. This is accomplished by elucidating the inherent tensor product structure within the Cartesian transverse degrees of freedom of light, which is the main resource for optical vector-matrix multiplication. To this end, we establish a discrete basis using localized Gaussian modes arranged in a lattice formation and demonstrate the operation of a Hadamard Gate. Leveraging the reprogrammable and digital capabilities of spatial light modulators, coupled with Fourier transforms by lenses, our approach proves adaptable to various algorithms. Therefore our work advances the use of structured light for quantum information processing.

quant-ph↗

arXiv2024

A Variational Approach to Learning Photonic Unitary Operators

Structured light, light tailored in its internal degrees of freedom, has become topical in numerous quantum and classical information processing protocols. In this work, we harness the high dimensional nature of structured light modulated in the transverse spatial degree of freedom to realise an adaptable scheme for learning unitary operations. Our approach borrows from concepts in variational quantum computing, where a search or optimisation problem is mapped onto the task of finding a minimum ground state energy for a given energy/goal function. We achieve this by a pseudo-random walk procedure over the parameter space of the unitary operation, implemented with optical matrix-vector multiplication enacted on arrays of Gaussian modes by exploiting the partial Fourier transforming capabilities of a cylindrical lens in the transverse degree of freedom for the measurement. We outline the concept theoretically, and experimentally demonstrate that we are able to learn optical unitary matrices for dimensions d = 2, 4, 8 and 16 with average fidelities of >90%. Our work advances high dimensional information processing and can be adapted to both process and quantum state tomography of unknown states and channels.

physics.optics↗