问题描述
已知实际门矩阵()和理想门矩阵(),求实际门的保真度。
思路
用QPT(quantum process tomography)将U矩阵转化为矩阵,然后求保真度。
具体的计算可直接调用qutip的相关接口。
实例
单比特门
理想门为,实际门为
矩阵形式:
计算过程:
import numpy as np
from scipy.linalg import expm
import qutip as qt
# 已知U_ideal和U_meas
U_ideal = expm(-1j/2*sigma_x*PI/2)
delta = 5/180*PI
U_meas = expm(-1j/2*sigma_x*(PI/2+delta))
# 将U矩阵转化为chi矩阵
U_psi_ideal = qt.Qobj(U_ideal)
U_rho_ideal = qt.spre(U_psi_ideal) * qt.spost(U_psi_ideal.dag())
U_psi_meas = qt.Qobj(U_meas)
U_rho_meas = qt.spre(U_psi_meas) * qt.spost(U_psi_meas.dag())
op_basis = [[qt.qeye(2), qt.sigmax(), qt.sigmay(), qt.sigmaz()]]
op_label = [["i", "x", "y", "z"]]
chi_ideal = qt.qpt(U_rho_ideal, op_basis)
chi_meas = qt.qpt(U_rho_meas, op_basis)
# 由chi矩阵计算门保真度
fidelity = qt.process_fidelity(qt.Qobj(chi_ideal),qt.Qobj(chi_meas))
print(f'fidelity:{fidelity:.4f}')
结果
fidelity:0.9981
注:有了矩阵,也可以直接可视化看一下QPT的结果。
# plot process tomography
fig = plt.figure(figsize=(14, 6))
ax1 = fig.add_subplot(121, projection='3d')
ax2 = fig.add_subplot(122, projection='3d')
qt.qpt_plot_combined(chi_ideal, op_label, ax=ax1)
qt.qpt_plot_combined(chi_meas, op_label, ax=ax2)
ax1.set_title(r'$U_{ideal}$')
ax2.set_title(r'$U_{meas}$')
两比特门
todo