Applying machine learning insights to quantum information challenges

Quantum information theory offers a rich landscape for exploring the intricacies of quantum computation and quantum systems. In this talk, we explore the confluence of machine learning techniques with diverse challenges in quantum information, touching upon Hamiltonian learning, measurement analysis, entanglement detection, algorithm design, and error-correcting codes construction. We highlight the use of transformer models to couple quantum state tomography with Hamiltonian learning, demonstrating its adaptability across different quantum systems. We also present a variational technique that brings clarity to the distinctions in pure-state tomography between uniquely determined and non-UD measurement schemes. Furthermore, variational strategies are harnessed to refine k-bosonic extensions for pinpointing entanglement, delve into the layers of quantum query complexity, and shape quantum error-correcting codes tailored for current quantum apparatuses. Throughout, the emphasis is on the potential of machine learning to address quantum information theory obstacles, signalling potential avenues for future exploration in quantum technologies.