Each text file that is a part of the dataset includes x and y data points used to produce the curves shown in Figures 3 and 4 from
https://arxiv.org/pdf/2510.13766. Upon downloading the dataset (the folder
data with 9 .txt files), the following code snippets reproduce these figures.
FIGURE 3:
import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 6))
fontsize_global = 18
plt.rcParams['font.size'] = fontsize_global
plt.rcParams['lines.linewidth'] = 3
linestyle = 'dashed'
output_path_sampled_rmse_exact_exponentials = 'data/sampled_rmse_exact_exponentials.txt'
data = np.loadtxt(output_path_sampled_rmse_exact_exponentials)
x = data[:, 0][:21]
y = data[:, 1][:21]
plt.plot(x, y, label='Exact exponentials', marker='o', linestyle=linestyle)
output_path_sampled_rmse_trotter_exponentials = 'data/sampled_rmse_trotter_exponentials_r_0.1t^2.txt'
data = np.loadtxt(output_path_sampled_rmse_trotter_exponentials)
x = data[:, 0]
y = data[:, 1]
plt.plot(x, y, label=r'PF, $r = \lceil 0.1 \tau^2 \rceil$', marker='*', linestyle=linestyle, markersize=11, color = 'orange')
output_path_sampled_rmse_trotter_exponentials = 'data/sampled_rmse_trotter_exponentials_r_0.05t^2.txt'
data = np.loadtxt(output_path_sampled_rmse_trotter_exponentials)
x = data[:, 0]
y = data[:, 1]
plt.plot(x, y, label=r'PF, $r = \lceil 0.05 \tau^2 \rceil$', marker='D', linestyle=linestyle, color = 'red')
output_path_sampled_rmse_trotter_exponentials = 'data/sampled_rmse_trotter_exponentials_r_500.txt'
data = np.loadtxt(output_path_sampled_rmse_trotter_exponentials)
x = data[:, 0]
y = data[:, 1]
plt.plot(x, y, label=r'PF, $r = 500$', marker='s', linestyle=linestyle, color = 'green')
plt.xlabel(r'Total Shots')
plt.ylabel(r'RMSE')
plt.tick_params(axis='both', which='major')
plt.tick_params(axis='both', which='minor')
plt.ylim(1e-2, 1e2)
plt.grid(True)
plt.tight_layout()
plt.legend(loc='lower left')
plt.yscale('log')
plt.xscale('log')
plt.show()
import numpy as np
import matplotlib.pyplot as plt
fontsize_global = 18
plt.rcParams['font.size'] = fontsize_global
plt.rcParams['lines.linewidth'] = 3
data1 = np.loadtxt('data/single_exponential_rte_t_1_r_100.txt')
x1, y1 = data1[:, 0], data1[:, 1]
data2 = np.loadtxt('data/single_exponential_rte_t_10_r_100.txt')
x2, y2 = data2[:, 0], data2[:, 1]
data3 = np.loadtxt('data/single_exponential_rte_t_20_r_100.txt')
x3, y3 = data3[:, 0], data3[:, 1]
data4 = np.loadtxt('data/single_exponential_rte_t_50_r_100.txt')
x4, y4 = data4[:, 0], data4[:, 1]
data5 = np.loadtxt('data/single_exponential_rte_t_80_r_100.txt')
x5, y5 = data5[:, 0], data5[:, 1]
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, figsize=(10, 10))
fig.subplots_adjust(hspace=0.15)
ax1.plot(x5, y5, label=r'$\tau^2/r = 64$', marker='D', linestyle='dashed',
color='C4', markersize=6, markevery=max(1, len(x5)//20))
ax1.set_yscale('log')
ax1.set_ylim(1e18, 2e20)
ax1.spines['bottom'].set_visible(False)
ax1.tick_params(bottom=False)
ax1.grid(True, which='major', alpha=0.5)
ax1.annotate('Examples of scenarios where\nthe simulation breaks down',
xy=(1e4, 1.5e19), xytext=(1e5, 3e19),
fontsize=14,
ha='center',
bbox=dict(boxstyle='round,pad=0.5', facecolor='yellow', alpha=0.7, edgecolor='black'),
arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0.3',
lw=2, color='black'))
ax2.plot(x4, y4, label=r'$\tau^2/r = 25$', marker='^', linestyle='dashed',
color='C3', markersize=7, markevery=max(1, len(x4)//20))
ax2.set_yscale('log')
ax2.set_ylim(2e6, 4e8)
ax2.spines['bottom'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax2.tick_params(bottom=False, top=False)
ax2.grid(True, which='major', alpha=0.5)
ax2.annotate('',
xy=(1e4, 4e7),
xytext=(1e5, 2.15e19),
xycoords='data',
textcoords=ax1.transData,
arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=-0.3',
lw=2, color='black'))
ax3.plot(x1, y1, label=r'$\tau^2/r = 0.01$', marker='o', linestyle='dashed',
color='C0', markersize=6, markevery=max(1, len(x1)//20))
ax3.plot(x2, y2, label=r'$\tau^2/r = 1$', marker='s', linestyle='dashed',
color='C1', markersize=6, markevery=max(1, len(x2)//20))
ax3.plot(x3, y3, label=r'$\tau^2/r = 4$', marker='*', linestyle='dashed',
color='C2', markersize=8, markevery=max(1, len(x3)//20))
ax3.set_xscale('log')
ax3.set_yscale('log')
ax3.set_ylim(1e-6, 1e1)
ax3.set_xlabel('RTE samples')
ax3.spines['top'].set_visible(False)
ax3.grid(True, which='major', alpha=0.5)
fig.supylabel('RMSE', x=0.02)
handles1, labels1 = ax1.get_legend_handles_labels()
handles2, labels2 = ax2.get_legend_handles_labels()
handles3, labels3 = ax3.get_legend_handles_labels()
ax3.legend(handles3 + handles2 + handles1, labels3 + labels2 + labels1,
loc='lower left', framealpha=0.6)
d = 0.015
kwargs = dict(color='k', clip_on=False, linewidth=1)
kwargs['transform'] = ax1.transAxes
ax1.plot((-d, +d), (-d, +d), **kwargs)
ax1.plot((1 - d, 1 + d), (-d, +d), **kwargs)
kwargs['transform'] = ax2.transAxes
ax2.plot((-d, +d), (1 - d, 1 + d), **kwargs)
ax2.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs)
ax2.plot((-d, +d), (-d, +d), **kwargs)
ax2.plot((1 - d, 1 + d), (-d, +d), **kwargs)
kwargs['transform'] = ax3.transAxes
ax3.plot((-d, +d), (1 - d, 1 + d), **kwargs)
ax3.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs)
plt.show()