paddle_quantum.biocomputing.algorithm

VQE algorithm to solve protein folding problem

class paddle_quantum.biocomputing.algorithm.ProteinFoldingSolver(penalty_factors, alpha, optimizer, num_iterations, tol=1e-08, save_every=1)

Bases: VQESolver

Parameters:

• penalty_factors (List[float]) – penalty factor [lambda0, lambda1] used in building the protein’s Hamiltonian.

• alpha (float) – the cutoff probability for CVaR expectation value calculation.

• optimizer (Optimizer) – paddle’s optimizer used to perform parameter update.

• num_iterations (int) – number of VQE iterations.

• tol (float) – convergence criteria.

• save_every (int) – number of steps between two recorded VQE loss.

solve(protein, ansatz, **optimizer_kwargs)

Run VQE to calculate the structure of the protein that satisfies various constraints.

Parameters:

• protein (Protein) – the protein structure to be optimized.

• ansatz (Circuit) – the quantum circuit represents the unitary transformation.

• optimizer_kwargs – see PaddlePaddle’s optimizer API for details https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Overview_cn.html (Chinese) or https://www.paddlepaddle.org.cn/documentation/docs/en/api/paddle/optimizer/Optimizer_en.html (English).

Returns:

A tuple contains the final loss value and optimal basis state.

Return type:

Tuple[float, str]