qrisp.vqe.VQEBenchmark.visualize#

VQEBenchmark.visualize(cost_metric='oqv', gain_metric='approx_ratio')[source]#

Plots the results of .evaluate.

Parameters:

cost_metricstr or callable, optional: The method to evaluate the cost of each run. The default is “oqv”.
gain_metricstr or callable, optional: The method to evaluate the gain of each run. The default is “approx_ratio”.

Examples

We create a Heisenberg problem instance and benchmark several parameters:

from networkx import Graph
G =Graph()
G.add_edges_from([(0,1),(1,2),(2,3),(3,4)])

from qrisp.vqe.problems.heisenberg import *

vqe = heisenberg_problem(G,1,0)
H = create_heisenberg_hamiltonian(G,1,0)

benchmark_data = vqe.benchmark(qarg = QuantumVariable(5),
                    depth_range = [1,2,3],
                    shot_range = [5000,10000],
                    iter_range = [25,50],
                    optimal_energy = H.ground_state_energy(),
                    repetitions = 2
                    )

To visualize the results, we call the corresponding method.

benchmark_data.visualize()

../../../../_images/vqe_benchmark_plot.png