Machine Learning-guided Synthesis of Quantum Entangled Materials

Abstract

The synthesis of materials into quantum entangled materials is a complicated challenge to an accurate and computational prediction of those materials. In this proposed work, it develops an AI-guided framework based on the combination between machine learning (ML) and reinforcement learning (RL), and quantum simulations to push the designing and validating of quantum materials at a much faster pace. In the first, graph neural networks (GNNs) are used to extract the atomic level quantum features, and in the second, generative models (VAE/GAN) are utilized to discover some novel entangled structures. In addition, fabrication with the synthesis parameters as parameters in the reinforcement learning results in an improvement of the experiment synthesis and a decrease of experiment failures as well as significant improvement of reproducibility. It demonstrates that the proposed hybrid ML-quantum simulation is validated on entanglement fidelity in real-world quantum computing platforms using IBM Qiskit and Google Cirq. As the proposed method is way beyond traditional ones, it has higher quantum coherence time, synthesis efficiency as well as higher prediction accuracy. In addition to enabling scaling-up of cryptography, quantum computing, and next generation nanomaterials, it is a cost and scalable framework for creating next generation quantum technologies applications as it is. And the model is further researched for the generalization in regards to real-time experimental feedback and for the expansion of the framework to a more general quantum materials program. The results show that AI approaches can truly accelerate the quantum material innovation even when syntheses are not at all possible.