Speaker
Description
We present an initial approach to applying a quantum support vector machine (QSVM) to the detection of gravitational waves. We explore the effect of the variation of the hyperparameters associated with quantum computing on the detection rate and compare the results with a classical support vector machine (SVM). The training and testing dataset is generated by injecting simulated events into noise samples from the detector L1. To reduce the dimension of the training samples, we generate features from the time series and use auto machine learning (AutoML) and permutation feature importance (PFI) to obtain a subset of the most significant features. Using this approach, we created a reduced set of features without sacrificing much of the accuracy of the SVM and allowed this use in the QSVM. Our experiments indicate that the method can achieve high detection rates and that the QSVM can achieve better accuracy than an optimized SVM algorithm when submitted to the same dataset.
