QEFMLA-FIDO: Quantum Evolution Formulation for ML Advancement: Fidelity-Improved Data Optimization in Quantum-Classical Systems

Using machine learning to improve quantum algorithms or quantum mechanics to improve machine learning is known as quantum machine learning. In order to include quantum physics into machine learning, we provide a unique model (QEFMLA-FIDO) in this research. The way an artificial neural network simulates the human brain is comparable to this method. Our suggested quantum-inspired technique can accelerate computationally challenging issues when executed on traditional hardware. Additionally, the method may easily be modified to operate on quantum technology, offering a far faster speedup than is achievable on conventional hardware. The Quantum Evolution Algorithm (QEA) improves computing efficiency by operating on both classical and quantum hardware and is motivated by the general development of quantum states. The technique solves complicated problems more quickly than conventional methods by iteratively evolving a system's Hamil-tonian to suit training data. It effectively enhances parameters by optimising the Quantum Fisher Information Matrix (QFIM). The QEA is suitable in real-world situations because it uses quantum parallelism to minimise computing complexity and is noise- resistant. It also has a predictive function that can detect improper outputs before they happen.