Optical Computing in Silicon Photonics: Self-Adapting Ring
Networks and Quantum Recurrent Neural Networks
P. Bienstman1, A. Lugnan1, S. Aggarwal2, F. Bruckerhoff-Pluckelmann3, W. H. P. Pernice3, H.
Bhaskaran2, C. Ma1, S. Sackesyn1, E. Gooskens1, S. Masaad1, M. Gouda1, R. De Prins1
1. Photonics Group, Department of Information Technology, Ghent University - imec,
Technologiepark 126, Ghent, 9052 Belgium, e-mail: peter.bienstman@ugent.be
2. University of Oxford, Department of Materials, Parks Road, Oxford OX1 3PH, UK
3. University of Munster, Department of Physics, CeNTech, Heisenbergstraße 11, 48149 Munster, Germany

Summary. We will present two different incarnations of silicon photonics for information
processing. The first one is a network of ring resonators with patches of phase changematerials, which experimentally shows synaptic plasticity. The second one is a recurrent
quantum neural network that is trained to perform a quantum version of optical channel
equalisation. We present simulations of a silicon quantum photonics network, and will
supplement this with experimental results on the Xanadu Borealis fibre setup.

Self-adapting ring networks
Synaptic plasticity, i.e. the ability of synaptic connections to strengthen or weaken depending on theirinput, is a fundamental component of learning and memory in biological neural networks [1]. This
property allows the network parameters to directly adapt to the input signal, thus without being
externally tuned by a training algorithm. In contrast with this paradigm, the most popular andsuccessful artificial neural network (ANN) models are nowadays based on backpropagation, whichusually requires full observability of the network states and precise parameter tuning. In practice, theserequirements strongly limit the scalability of neuromorphic hardware and backpropagation is notconsidered biologically plausible [2].
We present a novel all-optical recurrent ANN which can adapt to its input via synaptic plasticity. The
platform is integrated silicon photonics and plasticity is obtained through deposition of phase changematerial (namely GST [3]). We experimentally demonstrate the network employment as a plastic
reservoir for reservoir computing (RC), where the performance on a time series classification task isenhanced by letting the photonic network adapt to suitable input waveforms. Our network consists ofseveral coupled silicon ring resonators (RRs). One every three RRs is partially covered with GST andis used as plastic node with all-optical non-volatile memory [4]. Moreover, the RRs without GST are
employed as nonlinear neurons with multi-scale volatile memory, arising from the silicon nonlinear
effects in the ring waveguide [5,6].
In our experiment, we tackled the classification of 5 time series types, inserted as optical waveforms in
our RR network (Fig. 1(a)). The reservoir network generated several different nonlinear
representations of the input waveform, thus expanding the input dimensionality. Each output
waveform was integrated over time, allowing to employ slow electronics. The obtained waveform
energies were fed into the reservoir readout (linear classifier trained by logistic regression). The
classification performance was repeatedly evaluated via 6-fold cross-validation (Fig. 1(b)). Between
each evaluation, we performed a plastic adaptation step, consisting of the repeated insertion of amodified version of an input waveform class (a different class for each step). Each modified waveform
type could permanently change the configuration of the plastic weights (GST cells) in a different way.
We show (Fig. 1(b)) that the plastic adaptation steps could significantly improve the classification
performance, allowing to decrease the error from more than 40% to less than 10%. A similar trend wasobserved for other input wavelengths.

Figure. 1 (a) Outputs are detected and integrated over time, before being fed into a linear classifier. (b)Classification error rate after different plastic adaptation steps.

Quantum recurrent neural networks
We also have been investigating the reservoir computing paradigm in the context of online quantum
time series processing. More specifically, this means looking at tasks where both the input and theoutput are quantum states. This is in contrast to most other work in the field of quantum reservoir
computing, which considers tasks with a classical output. In the case of classical output, the reservoir
observables are typically expectation values, and therefore the system needs to be run multiple times
in order to estimate these expectation values. This results in a slow, unpractical experimental systemthat cannot perform online (i.e. real-time) processing of an incoming datastream. The task we havebeen studying is the quantum channel equalisation (QCE) task: given the output of a quantum channel
with memory, which imposes correlations and entanglement on an input stream, train a system to undo
these effects and generate an unentangled output stream. Fig. 2 visualises a setup that can be used totackle the QCE task: a decoder is trained to undo the effects of a randomly chosen encoder. We will
present experimental results on the Xanadu Borealis setup that illustrate this task.

Figure 2: Setup to solve the QCE task.

Acknowledgements
This research was funded by the EU grants Phresco, Fun-Comp, Nebula, Neoteric, Post-Digital andPrometheus and by the Belgian FWO (Flemish Fund for Scientific Research) grant G006020N.

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