Nearest-neighbour clustering is a powerful set of heuristic algorithms that find natural application in the decoding of signals transmitted using the$M$-Quadrature Amplitude Modulation ($M$-QAM) protocol. Lloyd et al. proposed a quantum version of the algorithm that promised an exponential speedup. We analyse the performance of this algorithm by simulating the use of a hybrid quantum-classical implementation of it upon 16-QAM and experimental 64-QAM data. We then benchmark the implementation against the classical k-means clustering algorithm. The choice of quantum encoding of the classical data plays a significant role in the performance, as it would for the hybrid quantum-classical implementation of any quantum machine learning algorithm. In this work, we use the popular angle embedding method for data embedding and the swap test for overlap estimation. The algorithm is emulated in software using Qiskit and tested on simulated and real-world experimental data. The discrepancy in accuracy from the perspective of the induced metric of the angle embedding method is discussed, and a thorough analysis regarding the angle embedding method in the context of distance estimation is provided. We detail an experimental optic fibre setup as well, from which we collect 64-QAM data. This is the dataset upon which the algorithms are benchmarked. Finally, some promising current and future directions for further research are discussed.

We describe an emerging application of data mining in the context of computer networks. This application concerns the problem of predicting the size of a flow and detecting elephant flows (very large flows). Flow size is a very important statistic that can be used to improve routing, load balancing and scheduling in computer networks. Flow size prediction is particularly challenging since flow patterns continuously change and predictions must be done in real time (milliseconds) to avoid delays. We describe how to formulate the problem as an online machine learning task to continuously adjust to changes in flow traffic. We evaluate the predictive nature of a set of features and the accuracy of three online predictors based on neural networks, Gaussian process regression and online Bayesian Moment Matching on three datasets of real traffic. We also demonstrate how to use such online predictors to improve routing (i.e., reduced flow completion time) in a network simulation.