Optimizing Quantum Support Vector Machine (QSVM) Circuits Using Hybrid Quantum Natural Gradient Descent (QNGD) and Whale Optimization Algorithm (WOA)

This research endeavor centers on augmenting the quantum circuitry of the Quantum Support Vector Machine (QSVM), a pivotal algorithm in the realm of quantum machine learning, via a hybrid optimization methodology that amalgamates Quantum Natural Gradient Descent (QNGD) with the Whale Optimization Algorithm (WOA). QSVM harnesses the power of quantum computing to facilitate proficient classification within high-dimensional spaces; however, its efficacy is frequently compromised by suboptimal circuit parameters and quantum noise prevalent in near-term quantum devices. The proposed hybrid optimization paradigm integrates the global search proficiency of WOA with the effective parameter refinement capabilities of QNGD, culminating in expedited convergence (38 iterations), enhanced classification accuracy (96.8%), optimized circuit complexity (44 quantum gates), and increased resilience against noise (fidelity: 0.985). In comparison to conventional optimization techniques such as Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Firefly Algorithm (FA), our methodology demonstrates superior efficacy by refining quantum kernel computation, entanglement configurations, and circuit depth, while concurrently alleviating the impacts of quantum noise.