QEFMLA-FIDO: Quantum Evolutionary Frameworks for Machine Learning Acceleration: Fisher Information-Driven Optimization in Hybrid Classical-Quantum Architectures

Quantum machine learning involves improving quantum algorithms using machine learning and/or improving machine learning using quantum mechanics. In this article, we propose a novel quantum evolutionary algorithm (QEA) that integrates quantum mechanics into machine learning. This algorithm is similar to how an artificial neural network mimics the human brain. Our proposed quantum-inspired algorithm can be run on classical hardware to provide with speedup for computationally hard problems. The algorithm is also readily suited to be extended for running on quantum hardware to provide with much improved speedup than possible on classical hardware. Inspired by the natural evolution of quantum states, the QEA operates on both classical and quantum hardware, enhancing computational efficiency. The algorithm iteratively evolves a system’s Hamiltonian to fit training data, solving complex problems faster than traditional methods. By maximizing the quantum Fisher information matrix (QFIM), it optimizes parameters efficiently. The QEA leverages quantum parallelism for reduced computational complexity and is robust to noise, making it applicable in real-world scenarios. Additionally, it features a predictive capability for identifying incorrect outputs before they occur.