Abstract
In this chapter, we discuss the complexity of the Hilbert space and the structure of matrix product state which naturally reduces the computational scaling. We have compared different methods of optimizing the matrix product state ansatz for a quantum chemical Hamiltonian. We have shown that machine learning can be a powerful tool in this context.
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