Fast algorithms for generating binary holograms

ArXiv (0)

D Stuart, O Barter, A Kuhn

We describe three algorithms for generating binary-valued holograms. Our methods are optimised for producing large arrays of tightly focussed optical tweezers for trapping particles. Binary-valued holograms allow us to use a digital mirror device (DMD) as the display element, which is much faster than other alternatives. We describe how our binary amplitude holograms can be used to correct for phase errors caused by optical aberrations. Furthermore, we compare the speed and accuracy of the algorithms for both periodic and arbitrary arrangements of traps, which allows one to choose the ideal scheme depending on the circumstances.

EIT-Based Quantum Memory for Single Photons from Cavity-QED

ArXiv (0)

M Himsworth, P Nisbet, J Dilley, G Langfahl-Klabes, A Kuhn

We investigate the feasibility of implementing an elementary building block for quantum information processing. The combination of a deterministic single photon source based on vacuum stimulated adiabatic rapid passage, and a quantum memory based on electromagnetically induced transparency in atomic vapour is outlined. Both systems are able to produce and process temporally shaped wavepackets which provides a way to maintain the indistinguishability of retrieved and original photons. We also propose an efficient and robust `repeat-until-success' quantum computation scheme based on this hybrid architecture.

Strongly Coupled Atom-Cavity Systems

in Quantum Information Processing, Wiley-VCH Verlag GmbH & Co. KGaA (0) 223-236

A Kuhn, M Hennrich, G Rempe

Learning Group Structure and Disentangled Representations of Dynamical Environments

ArXiv (0)

R Quessard, TD Barrett, WR Clements

Discovering the underlying structure of a dynamical environment involves learning representations that are interpretable and disentangled, which is a challenging task. In physics, interpretable representations of our universe and its underlying dynamics are formulated in terms of representations of groups of symmetry transformations. We propose a physics-inspired method, built upon the theory of group representation, that learns a representation of an environment structured around the transformations that generate its evolution. Experimentally, we learn the structure of explicitly symmetric environments without supervision while ensuring the interpretability of the representations. We show that the learned representations allow for accurate long-horizon predictions and further demonstrate a correlation between the quality of predictions and disentanglement in the latent space.

Backpropagation through nonlinear units for all-optical training of neural networks

ArXiv (0)

X Guo, TD Barrett, ZM Wang, AI Lvovsky

Backpropagation through nonlinear neurons is an outstanding challenge to the field of optical neural networks and the major conceptual barrier to all-optical training schemes. Each neuron is required to exhibit a directionally dependent response to propagating optical signals, with the backwards response conditioned on the forward signal, which is highly non-trivial to implement optically. We propose a practical and surprisingly simple solution that uses saturable absorption to provide the network nonlinearity. We find that the backward propagating gradients required to train the network can be approximated in a pump-probe scheme that requires only passive optical elements. Simulations show that, with readily obtainable optical depths, our approach can achieve equivalent performance to state-of-the-art computational networks on image classification benchmarks, even in deep networks with multiple sequential gradient approximations. This scheme is compatible with leading optical neural network proposals and therefore provides a feasible path towards end-to-end optical training.