I thing with Quantum machine learnig you can solve

The answer is affirmative. The path follows Regge calculus, tackles Laplacians on discrete and quantum geometries, links with the Dirac operator and culminates in topological machine learning.

there isn't a well-established connection between quantum computing and polyhedral chains in the context of machine learning. Quantum computing primarily focuses on leveraging the principles of quantum mechanics to perform computations in a fundamentally different way than classical computers.

Polyhedral chains, on the other hand, are structures used in mathematics and geometry to represent certain topological properties of polyhedra. They are often used in algebraic topology to study the shape of spaces and their transformations.

That being said, research and developments in both quantum computing and various mathematical structures are ongoing, and interdisciplinary connections are always possible. If there has been progress in linking quantum computing with polyhedral chains after my last update, it would be advisable to check the latest literature, research papers, and conference proceedings in both quantum computing and mathematics domains.

Quantum machine learning is an emerging field that explores the potential of quantum computing to enhance machine learning algorithms. It often involves using quantum algorithms to solve certain machine learning problems more efficiently than classical algorithms. Polyhedral chains, if relevant, might find connections in the broader landscape of quantum algorithms or quantum-enhanced optimization techniques used in machine learning.

To stay updated on the latest developments, you may want to explore recent research publications in quantum computing, quantum machine learning, and mathematical structures such as polyhedral chains.

yes,quantum optimization intersection with quantum simulation can play a crucial role