def d(x, rho_2):
# x specifies rho = T T^
rho_1 = np.matmul(np.array([[x[0], 0], [x[1] + 1j * x[2], x[3]]]),
np.array([[x[0], x[1] - 1j * x[2]], [0, x[3]]]))
distance = sts.distance_trace(rho_1, rho_2)
return distance
# Step 6: Estimate density matrix
#
# Compute linear estimator using
# the M1, M2, M3 measurements
#
dens_est = estimation.linear_estimate_adapt(M1_data, M2_data, M3_data,
M1, M2, M3)
# Step 4: Compute and the distances
#
# Compute distances between the estimated
# and true density matrix using the
# different distance fuctions.
#
dist_op[n] = stats.distance_op(dens, dens_est)
dist_trace[n] = stats.distance_trace(dens, dens_est)
dist_fid[n] = stats.distance_fid(dens, dens_est)
# Count the number of non-physical matrices
#
eigenvalues = np.linalg.eigvals(dens_est)
if eigenvalues[0] < 0 or eigenvalues[1] < 0:
non_physical_count = non_physical_count + 1
# Step 5: Average the distances
#
# Average the distances for each value of x
#
av_distances[k, :] = [
np.mean(dist_op),
np.mean(dist_trace),