EXPERIMENTAL EVALUATION OF QUANTUM MACHINE LEARNING ALGORITHMS USING QISKIT

Alessandro Luongo

Amit A. Bhusari, Prabhanjan Chaudhari, Dipali Bhusari +1 more

Quantum algorithms for machine learning represent a paradigm shift in computational learning. While still in the early stages, QAML holds the promise to transform industries by enabling faster, more powerful models — especially for problems that are intractable for classical systems. As quantum hardware and algorithms improve, QAML will likely play a central role in the future of artificial intelligence.

Amani Lamine, Ameni Mejri, Bilel Charfi

MA NANNAN

Nimish Mishra, Manik Kapil, H R Rakesh +20 more

Journal of Theoretical and Applied Information Technology

Classical computational approaches are steadily improving in their ability to predict molecular properties based on their sequences or structure, which is essential in drug discovery, irrespective of its many challenges. The major challenge is to use computational approaches to identify the properties, such as toxicity, solubility, etc. (ADMET), of the drug targets without costing much time. Machine learning and deep learning methods are employed for predicting ADMET properties. The proposed approach combines quantum computing with machine learning for the binary task of toxicity prediction based on the SMILES data. This Quantum Machine Learning (QML) approach involves a novel five-step procedure that converts SMILES to their respective molecular fingerprints, which are in turn dimensionally reduced, consequently forming four qubits. These qubits are inputs to the QML models, such as the Quantum SVC (QSVC), Variational Quantum Circuits (VQC), and Quantum AutoEncoders (QAE). The models are trained and optimized by using different optimizers and then evaluated based on the accuracy metric. The QAE model outperformed the remaining model by achieving an accuracy of 99%.