Quantum Mechanics and Machine Learning

George Chapline

Dixit Vimal

Quantum Machine Learning (QML) which is the integration of Quantum Mechanics (QM) and Machine Learning (ML), comes with immense computational capacity due to principles of superposition and entanglement. The PIMA diabetes dataset is used in this research as a test bed for comparing the results obtained from ML and QML approaches. The assessment shows that Support Vector Machine (SVM) has better results compared to Logistic Regression (LR) with an accuracy of 0.76. On the other hand, among QML algorithms; the Quantum Support Vector Classifier (QSVC) has a higher accuracy than the Variational Quantum Classifier (VQC) with 0.74. The findings of the given research imply that the use of QML methods may be valuable for further developments in diabetes prediction in the future.

Alejandro López‐Bezanilla, Louis-François Arsenault, Andrew J. Millis +2 more

Tarun Kumar Vashishth, Vikas Sharma, Kewal Krishan Sharma +1 more

Quantum machine learning (QML) is an emerging interdisciplinary field that combines the principles of quantum mechanics with machine learning algorithms, offering promising solutions to complex computational problems. This paper provides an extensive introduction to quantum machine learning, exploring the synergies between artificial intelligence and quantum technology. It begins by elucidating the foundational concepts of quantum computing, including quantum bits (qubits), quantum gates, superposition, and entanglement. Subsequently, the paper delves into the basic principles of machine learning and the challenges faced by classical methods in handling large-scale data and solving computationally intensive tasks. With a strong foundation in place, the focus shifts towards the integration of quantum mechanics with machine learning, showcasing the advantages and potential breakthroughs QML offers over classical approaches. The exploration of quantum algorithms for machine learning, such as quantum support vector machines (QSVM), quantum neural networks (QNN), and quantum variational algorithms, reveals the power of quantum-enhanced techniques in data classification, regression, and clustering.

Pengfei Zhang

Sushamana Sharma

The field of quantum mechanics is yet to be explored fully but still has great applications particularly in quantum information processing. The need of alliance of quantum mechanics and machine learning (ML) is discussed. The speed up achieved in quantum computation is the main reason of looking forward to use quantum mechanics to reduce time complexity in learning process. Quantum superposition seems to be able to reduce sample complexity of ML algorithm by reducing number of examples required to learn a task. The hurdles in this path and the possible techniques to conquer these are examined at the understanding level. The main aspects of quantum ML are discussed.