Quantum Machine Intelligence

Quantum computing has turned out to be a paradigm shift that can address computational problems that are way out of the scope of classical systems. The current hybrid designs tend to view quantum and classical components as non-living, and do not provide the feedback intelligence to control energy, depth and computational balance in real-time. The present paper presents a Quantum Machine Intelligence (QMI) model, which will integrate quantum computation, artificial intelligence as well as reinforcement learning into a self-regulated system. The model is developed on quantum encoding and amplitude-based encoding, a reinforcement-learning controller using hybrid optimization variations, and design to adjust simultaneously the depth of the circuits and the number of qubits that are used to evaluate the performance of a component Empirical analyses show that QMI attains 98.3% accuracy, 17.8% energy saving, and 15 times quick convergence in opposition to the latest DRL, hybrid DL, and LLM-aided baselines.