Quantum machine learning for particle physics using a variational quantum classifier

He-Xing Yin, Zhiyuan Hu, Huan-Huan Zeng +2 more

Bhavna Saini, Vivek Kumar Verma, Nidhi Kushwaha +1 more

Andrew Blance, Michael Spannowsky

A bstract Quantum machine learning aims to release the prowess of quantum computing to improve machine learning methods. By combining quantum computing methods with classical neural network techniques we aim to foster an increase of performance in solving classification problems. Our algorithm is designed for existing and near-term quantum devices. We propose a novel hybrid variational quantum classifier that combines the quantum gradient descent method with steepest gradient descent to optimise the parameters of the network. By applying this algorithm to a resonance search in di-top final states, we find that this method has a better learning outcome than a classical neural network or a quantum machine learning method trained with a non-quantum optimisation method. The classifiers ability to be trained on small amounts of data indicates its benefits in data-driven classification problems.

Kosuke Mitarai

Daniel Sierra-Sosa, Juan D. Arcila-Moreno, Begonya García-Zapirain +2 more

Dementia is the fifth cause of death worldwide with 10 million new cases every year. Healthcare applications using machine learning techniques have almost reached the physical limits while more data is becoming available resulting from the increasing rate of diagnosis. Recent research in Quantum Machine Learning (QML) techniques have found different approaches that may be useful to accelerate the training process of existing machine learning models and provide an alternative to learn more complex patterns. This work aims to report a real-world application of a Quantum Machine Learning Algorithm, in particular, we found that using the implemented version for Variational Quantum Classiffication (VQC) in IBM's framework Qiskit allows predicting dementia in elderly patients, this approach proves to provide more consistent results when compared with a classical Support Vector Machine (SVM) with a linear kernel using different number of features.

Shreshtha Misra, Poonam Rani

The emerging field of Quantum Machine learning (QML), which offers noticeably faster processing speed, is the result of the merger of Quantum Computing (QC) and Machine Learning (ML). Superposition and Entanglement are the fundamental concepts of QC, which increases computational scalability. This comprehensive overview examines QML foundations, algorithms, applications, and challenges of QML. When applied to the Cleveland Heart Disease Dataset, the Quantum Support Vector Classifier (QSVC) achieves an accuracy of 0.75 whereas the Variational Quantum Classifier (VQC) yields an accuracy of 0.62, thus showing that QSVC outperforms VQC. The main issue in QML that calls for effort and investment is noise. QML has the potential to advance technology and has its application in a variety of domains.