An Experimental Comparison of Qiskit and Pennylane for Hybrid Quantum-Classical Support Vector Machines

Quantum computing holds great promise for enhancing machine learning algorithms, particularly by integrating classical and quantum techniques. This study compares two prominent quantum development frameworks, Qiskit and Pennylane, focusing on their suitability for hybrid quantum-classical support vector machines with quantum kernels. Our analysis reveals that Qiskit requires less theoretical information to be used, while Pennylane demonstrates superior performance in terms of execution time. Although both frameworks exhibit variances, our experiments reveal that Qiskit consistently yields superior classification accuracy compared to Pennylane when training classifiers with quantum kernels. Additionally, our results suggest that the performance of both frameworks remains stable for up to 20 qubits, indicating their suitability for practical applications. Overall, our findings provide valuable insights into the strengths and limitations of Qiskit and Pennylane for hybrid quantum-classical machine learning.