The rapid pace of development over the last few decades in the domain of machine learning mirrors the advances made in the field of quantum computing. It is natural to ask whether the conventional machine learning algorithms could be optimized using the present-day noisy intermediate-scale quantum technology. There are certain computational limitations while training a machine learning model on a classical computer. Using quantum computation, it is possible to surpass these limitations and carry out such calculations in an optimized manner. (...) This study illustrates the working of the quantum support vector machine classification model which guarantees an exponential speed-up over its typical alternatives. This research uses the quantum SVM model to solve the classification task of a malignant breast cancer diagnosis. This study also demonstrates a comparative analysis of distinct forms of SVM algorithms concerning their time complexity and performances on standard evaluation metrics, namely accuracy, precision, recall, and F1-score, to exemplify the supremacy of quantum SVM over its conventional variants. (shrink)

Cognitive control enables people to adjust their thoughts and actions according to the current task demands. Response inhibition and response adjustment are two key aspects of cognitive control. Here, we examined how the implicit processing of emotional information influences these two functions with the help of the double-step saccade task. Each trial had either a single target or two sequential targets. Upon a single target onset, participants were required to make a quick saccade, but upon two target onsets, participants were (...) instructed to inhibit their initial saccades and redirect their gaze to the second target. In three experiments, we manipulated the emotional information of the first and second targets. We found that irrelevant emotional information of the first target impaired response inhibition compared to non-emotional information (geometric shapes) of the first target. When non-emotional information (geometric shape) came as the first target, irrelevant angry emotional faces as the second target interfered with both response inhibition and response adjustment compared to irrelevant happy and neutral faces. We explain these results with previous findings that processing faces with irrelevant angry facial expressions take up many attentional resources, leaving fewer resources available for ongoing activities such as response inhibition and response adjustment. (shrink)

Ethical morality is one of the significant issues in self-driving cars. The paper provides a newer approach to solve the ethical decision problems in self-driving cars until there is no concrete ethical decision to all problems. This paper gives a two-way approach to solve a problem, with first being the mapping of problem to the solution already known or which has a fixed set of solutions and action priorities defined to a problem previously. Now, if no solution is found or (...) mapping is unsuccessful, then the second stage activates, where the solution from Deep Q-learning model is calculated. It estimates the best Q value and returns that solution or action which maximizes the reward at that instance. The reward function is designed with decreasing priorities and acts accordingly, where the users can change or define their priorities if needed. The case study and results show that the solution that is present in the paper will lead to solving ethical morality problems in self-driving cars up to a great extent. (shrink)