Toward Automated Quantum Variational Machine Learning
This work addresses the challenge of optimizing quantum variational circuits for researchers in quantum machine learning, though it appears incremental as it builds on existing methods.
The paper tackles the problem of automating quantum variational machine learning by developing MUSE, a multi-locality parallelizable search algorithm, which improves quantum variational classifier accuracy by 2.3 times on average and enhances regression predictions from negative to positive coefficients of determination.
In this work, we address the problem of automating quantum variational machine learning. We develop a multi-locality parallelizable search algorithm, called MUSE, to find the initial points and the sets of parameters that achieve the best performance for quantum variational circuit learning. Simulations with five real-world classification datasets indicate that on average, MUSE improves the detection accuracy of quantum variational classifiers 2.3 times with respect to the observed lowest scores. Moreover, when applied to two real-world regression datasets, MUSE improves the quality of the predictions from negative coefficients of determination to positive ones. Furthermore, the classification and regression scores of the quantum variational models trained with MUSE are on par with the classical counterparts.