Comparison of Classification Models of Novel Quantum Machine Learning on QisKit Dataset

Abstract

Classical computers utilize ones and zeroes, but quantum computers employ qubits. Quantum computers employ ones and zeros, but qubits have a third state called “superposition” that lets them represent both at once. In quantum computing, qubits can serve double duty. A quantum computer functions under different rules than a regular computer. It doesn't use bits and bytes like ordinary computers, but qubits. Quantum computers measure and retrieve data using qubits. Unlike standard computer bits, qubits can hold several values at once. They have a speed advantage over traditional computers and algorithms. Quantum machine learning uses qubits, quantum processes, or other specialized quantum systems to boost algorithm computation and storage speed. Data is computed using machine learning methods. Quantum machine learning adds quantum computers to the pool of machine learning gear. Quantum computing exploits quantum theory to handle information. Three quantum machine learning models were constructed for dataset categorization to test quantum computing. Comparing quantum and classical ML models. Quantum ML models showed the most accuracy after analysis. QSVM got 98%, QLR and QKNN 97%. Quantum deep learning can also be used for future investigations.

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