Quantum intelligence on protein folding pathways*

Abstract

We study the protein folding problem on the base of the quantum approach we proposed recently by considering the model of protein chain with nine amino-acid residues. We introduced the concept of distance space and its projections on a XY -plane, and two characteristic quantities, one is called compactness of protein structure and another is called probability ratio involving shortest path. Our results not only confirmed the fast quantum folding time but also unveiled the existence of quantum intelligence hidden behind in choosing protein folding pathways. *

Variational quantum semi-supervised classifier based on label propagation

“…Therefore, VQAs have lower requirements for quantum resources and have become important methods for implementing quantum machine learning tasks. At present, VQAs have been applied in classification, [2][3][4][5] clustering, [6,7] generative models, [8][9][10] dimensionality reduction, [11][12][13] etc.…”

“…, where the subscripts 1, 2 and 3 represent three registers. Let ρ = |ψ ψ| be the density matrix of |ψ , then E( f ) can be written as (7) where the reduced density matrix ρ 1 = tr 2,3 (ρ) is got by tracing registers 2 and 3, proportional to the matrix L = B BT . |ϕ f (θ ) is the state of the label vector f built by the parameterized quantum circuit, where θ is the trainable parameter set.…”