This work evaluates the performance of both Neural Networks (NNs) and Parameterized Quantum Circuits (PQCs) in Reinforcement Learning environments. We present novel quantum gate based games that...Show moreThis work evaluates the performance of both Neural Networks (NNs) and Parameterized Quantum Circuits (PQCs) in Reinforcement Learning environments. We present novel quantum gate based games that can run on current NISQ hardware. Our results show that NNs and PQCs achieve very similar performance distributions across the different environments, showing the promise of PQCs for (Quantum) Machine Learning applications. It is also shown that the NNs tested are more sensitive to change in learning rate than our PQC models. NN performance is also more eratic with relation to the amount of parameters than PQC performance, showing hyperparameter tuning might be more predictable for PQCs. Lastly, the smallest PQC designs show strong performance, often outperforming NNs with more parameters.Show less
Electroencephalography (EEG) has been used for over a century to measure electric potentials in the brain. The brain signals can be used in a Brain Computer Interface (BCI) to control machines,...Show moreElectroencephalography (EEG) has been used for over a century to measure electric potentials in the brain. The brain signals can be used in a Brain Computer Interface (BCI) to control machines, like prostheses or exoskeletons. In the last decades, consumer-grade EEG devices have become available. Their performance and reliability, however, does not match that of medical EEG equipment. In this project, the possibilities for classification with such a consumer-grade EEG device, the EMOTIV EPOC+, were explored. The main challenge was to overcome the large noise and nuisances in the EEG signal to be able to classify brain states induced by motoric tasks and sensory stimuli. The effect of several preprocessing techniques was studied: Independent Component Analysis, a denoising autoencoder, and adaptive filtering with an autoencoder. For the classification, various machine learning approaches were proposed to analyse the EEG signals, ranging from a naive Bayes classifier to Deep Convolutional Neural Networks. Furthermore, the effect of motivating the structure of a neural network for this classification task was investigated. It was found that using the spectral features of the EEG signal as a motivation for the structure of neural networks helps classifiers identify brain states that are not classified by other types of neural networks.Show less
