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‎README.md‎

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[![License](https://img.shields.io/github/license/Jue-Xu/Quantum-Simulation-Recipe.svg?style=popout-square)](https://opensource.org/licenses/Apache-2.0)
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[![Release](https://img.shields.io/github/v/release/jue-xu/Quantum-Simulation-Recipe?include_prereleases)](https://github.com/Jue-Xu/Quantum-Simulation-Recipe/releases)
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This python package (published on [PiP](https://pypi.org/project/quantum-simulation-recipe/)) contains ingredients for quantum simulation, such as the Hamiltonians and algorithmic primitives, mainly build on [qiskit](https://www.ibm.com/quantum/qiskit), [openfermion](https://github.com/quantumlib/OpenFermion), etc.
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This python package (published on [PiP](https://pypi.org/project/quantum-simulation-recipe/)) contains basic ingredients for quantum simulation, such as the Hamiltonians and algorithmic primitives, mainly build on [qiskit](https://www.ibm.com/quantum/qiskit), [openfermion](https://github.com/quantumlib/OpenFermion), etc.
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## Install
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```bash

‎quantum_simulation_recipe/plot_config.py‎

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import numpy as np
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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from matplotlib.colors import ListedColormap
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import colorsys
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default_color_cycle = ["#B65655FF", "#5471abFF", "#6aa66eFF", "#A66E6AFF"]
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# Function to lighten a color
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def lighten_color(color, amount=0.3):
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# Convert color from hexadecimal to RGB
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r, g, b, a = tuple(int(color[i:i+2], 16) for i in (1, 3, 5, 7))
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# Convert RGB to HLS
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h, l, s = colorsys.rgb_to_hls(r/255, g/255, b/255)
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# Lighten the luminance component
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l = min(1, l + amount)
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# Convert HLS back to RGB
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r, g, b = tuple(round(c * 255) for c in colorsys.hls_to_rgb(h, l, s))
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# Convert RGB back to hexadecimal
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new_color = f"#{r:02x}{g:02x}{b:02x}{a:02x}"
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return new_color
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def set_color_cycle(color_cycle, alpha=0.3, mfc=True):
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color_cycle_light = [lighten_color(color, alpha) for color in color_cycle]
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if mfc:
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colors = mpl.cycler(mfc=color_cycle_light, color=color_cycle, markeredgecolor=color_cycle)
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else:
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colors = mpl.cycler(color=color_cycle, markeredgecolor=color_cycle)
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mpl.rc('axes', prop_cycle=colors)
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set_color_cycle(default_color_cycle)
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# mpl.rc('axes', grid=True, edgecolor='k', prop_cycle=colors)
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# mpl.rcParams['axes.prop_cycle'] = colors
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# mpl.rcParams['lines.markeredgecolor'] = 'C'
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mpl.rcParams['font.family'] = 'sans-serif' # 'Helvetica'
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mpl.rcParams['axes.linewidth'] = 1.5
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mpl.rcParams["xtick.direction"] = 'out' # 'out'
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mpl.rcParams["ytick.direction"] = 'out'
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mpl.rcParams['xtick.major.width'] = 1.5
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mpl.rcParams['ytick.major.width'] = 1.5
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mpl.rcParams['ytick.minor.width'] = 1.5
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mpl.rcParams['lines.markersize'] = 11
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mpl.rcParams['legend.frameon'] = True
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mpl.rcParams['lines.linewidth'] = 1.5
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# plt.rcParams['lines.markeredgecolor'] = 'k'
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mpl.rcParams['lines.markeredgewidth'] = 1.5
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mpl.rcParams['figure.dpi'] = 100
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mpl.rcParams['figure.figsize'] = (8, 6)
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mpl.rcParams['figure.autolayout'] = True
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mpl.rcParams['axes.grid'] = True
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mpl.rcParams['savefig.bbox'] = 'tight'
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mpl.rcParams['savefig.transparent'] = True
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SMALL_SIZE = 14
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MEDIUM_SIZE = 18 #default 10
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LARGE_SIZE = 24
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# MARKER_SIZE = 10
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plt.rc('font', size=MEDIUM_SIZE) # controls default text sizes
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plt.rc('axes', titlesize=LARGE_SIZE+2) # fontsize of the axes title
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plt.rc('axes', labelsize=LARGE_SIZE) # fontsize of the x and y labels
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plt.rc('xtick', labelsize=LARGE_SIZE) # fontsize of the tick labels
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plt.rc('ytick', labelsize=LARGE_SIZE) # fontsize of the tick labels
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plt.rc('legend', fontsize=MEDIUM_SIZE-2) # legend fontsize
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plt.rc('figure', titlesize=LARGE_SIZE) # fontsize of the figure title
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# def data_plot(x, y, marker, label, alpha=1, linewidth=1, loglog=True, markeredgecolor='black'):
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# if loglog:
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# plt.loglog(x, y, marker, label=label, linewidth=linewidth, markeredgecolor=markeredgecolor, markeredgewidth=0.5, alpha=alpha)
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# else:
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# plt.plot(x, y, marker, label=label, linewidth=linewidth, markeredgecolor=markeredgecolor, markeredgewidth=0.5, alpha=alpha)
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from scipy.optimize import curve_fit
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from math import ceil, floor, log, exp
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def linear_loglog_fit(x, y, verbose=False):
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# Define the linear function
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def linear_func(x, a, b):
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return a * x + b
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log_x = np.array([log(n) for n in x])
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log_y = np.array([log(cost) for cost in y])
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# Fit the linear function to the data
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params, covariance = curve_fit(linear_func, log_x, log_y)
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# Extract the parameters
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a, b = params
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# Predict y values
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y_pred = linear_func(log_x, a, b)
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# Print the parameters
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if verbose: print('Slope (a):', a, '; Intercept (b):', b)
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exp_y_pred = [exp(cost) for cost in y_pred]
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return exp_y_pred, a, b
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def plot_fit(ax, x, y, var='t', x_offset=1.07, y_offset=1.0, label='', ext_x=[], linestyle='k--', linewidth=1.5, fontsize=MEDIUM_SIZE, verbose=True):
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y_pred_em, a_em, b_em = linear_loglog_fit(x, y)
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if verbose: print(f'a_em: {a_em}; b_em: {b_em}')
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if abs(a_em) < 1e-3:
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text_a_em = "{:.2f}".format(round(abs(a_em), 4))
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else:
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text_a_em = "{:.2f}".format(round(a_em, 4))
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if ext_x != []: x = ext_x
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y_pred_em = [exp(cost) for cost in a_em*np.array([log(n) for n in x]) + b_em]
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if label =='':
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ax.plot(x, y_pred_em, linestyle, linewidth=linewidth)
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else:
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ax.plot(x, y_pred_em, linestyle, linewidth=linewidth, label=label)
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ax.annotate(r'$O(%s^{%s})$' % (var, text_a_em), xy=(x[-1], np.real(y_pred_em)[-1]), xytext=(x[-1]*x_offset, np.real(y_pred_em)[-1]*y_offset), fontsize=fontsize)
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return a_em, b_em
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def ax_set_text(ax, x_label, y_label, title=None, legend='best', xticks=None, yticks=None, grid=None, log='', ylim=None):
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ax.set_xlabel(x_label)
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ax.set_ylabel(y_label)
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if title: ax.set_title(title)
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if legend: ax.legend(loc=legend)
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if log == 'x':
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ax.set_xscale('log')
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elif log == 'y':
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ax.set_yscale('log')
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elif log == 'xy':
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# ax.set_xscale('log')
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# ax.set_yscale('log')
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ax.loglog()
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else:
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pass
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if grid: ax.grid()
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if ylim: ax.set_ylim([ylim[0]*0.85, ylim[1]*1.15])
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if xticks is not None:
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ax.set_xticks(xticks)
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ax.get_xaxis().set_major_formatter(mpl.ticker.ScalarFormatter())
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if yticks is not None:
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ax.set_yticks(yticks)
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ax.get_yaxis().set_major_formatter(mpl.ticker.ScalarFormatter())
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def matrix_plot(M):
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fig, ax = plt.subplots()
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real_matrix = np.real(M)
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# Plot the real part using a colormap
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ax.imshow(real_matrix, cmap='RdYlBu', interpolation='nearest', origin='upper')
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# Create grid lines
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ax.grid(True, which='both', color='black', linewidth=1)
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# Add color bar for reference
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cbar = plt.colorbar(ax.imshow(real_matrix, cmap='RdYlBu', interpolation='nearest', origin='upper'), ax=ax, orientation='vertical')
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cbar.set_label('Real Part')
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# Add labels to the x and y axes
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plt.xlabel('X-axis')
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plt.ylabel('Y-axis')
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# Show the plot
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plt.title('Complex Matrix with Grid')
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plt.show()

‎tests/test.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 2,
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"id": "6dd25106",
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"metadata": {},
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"outputs": [
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"# packages in environment at /opt/homebrew/Caskroom/miniforge/base:\n",
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"#\n",
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"# Name Version Build Channel\n",
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"quantum-simulation-recipe 0.0.9 pypi_0 pypi\n"
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"quantum-simulation-recipe 0.1.2 pypi_0 pypi\n"
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]
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}
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],
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 1,
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"id": "8c03d3e4",
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"metadata": {},
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"outputs": [
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" coeffs=[1.+0.j, 1.+0.j, 1.+0.j])]"
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]
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},
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"execution_count": 13,
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import quantum_simulation_recipe as qsr\n",
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"from quantum_simulation_recipe import spin_ham\n",
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"from quantum_simulation_recipe import spin\n",
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"\n",
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"H = spin_ham.Nearest_Neighbour_1d(4)\n",
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"H = spin.Nearest_Neighbour_1d(4)\n",
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"H.ham\n",
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"H.ham_xyz"
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]

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