Fig. 2: Schematic representation of the folding algorithm and folding process. | npj Quantum Information

Fig. 2: Schematic representation of the folding algorithm and folding process.

From: Resource-efficient quantum algorithm for protein folding

Fig. 2

a Starting from a random population (up-center) of circuit parameters {θ}, every parent, θp, undergoes a parametrized recombination with other individuals according to the procedure detailed in Section “Methods”. The corresponding trial wavefunctions are generated in the quantum circuit as described in the main text and measured to estimate the new CVaRs. They determine the selection criteria of whether to replace a parent by its offspring for the new generation. b Folding of the ten amino acid Angiotensin peptide. Energy distribution at the convergence of the low-energy folds for the population obtained with the CVaR-VQE algorithm and the DE optimizer. The results were obtained using 128 (blue) and 1024 measurements (orange). Simulations were carried out using a realistic parametrization of the noise. The binary strings (q1,6q1,8q1,10q2,7q2,9q3,8q3,10q4,9q5,10) associated with the different bars represent the contact qubits (see text) that entirely define the conformation energies. The numbers labeling the bars correspond to the exact degeneracy of the conformations. The total probabilities of finding low-energy conformations (energy below 0) adds up to 89.5% (small sampling, blue) and 100% (large sampling, orange). The fittest individual in the population collapses to the ground state with a probability of 42.2% (Supplementary Methods, Fig. 2). c Primary sequence of Angiotensin. To each amino acid is assigned a color that characterizes its specific physical properties. The letters stand for Aspartic-Acid (D), Arginine (R), Valine (V), Tyrosine (Y), Histidine (H), Proline (P), Phenylalanine (F), and Leucine (L). d Pairwise interaction matrix for Angiotensin constructed using the MJ model (Table 3 in17).

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