def plot_result(result):
# Get physical parameters for the simulation
qubit_count = result["qubit_count"]
segment_count = result["segment_count"]
durations = get_durations(segment_count)
local_shifts = get_local_shifts(qubit_count)
omega_signal = result["omega_segments"]
delta_signal = result["delta_segments"]
omega_segments = np.array([sig["value"] for sig in omega_signal])
delta_segments = np.array([sig["value"] for sig in delta_signal])
fixed_segment = np.array([1])
krylov_subspace_dimension = result["krylov_subspace_dimension"]
# Create initial and target states
initial_state = create_initial_state(qubit_count)
target_state = create_target_state(qubit_count)
with qctrl.create_graph() as graph:
# Calculate terms and put together the full Hamiltonian
H_omega, H_delta, H_fixed = get_rydberg_hamiltonian_terms(qubit_count)
hamiltonian = qctrl.operations.sparse_pwc_sum([
qctrl.operations.sparse_pwc_operator(
signal=qctrl.operations.tensor_pwc(np.array(durations),
omega_segments),
operator=H_omega,
),
qctrl.operations.sparse_pwc_operator(
signal=qctrl.operations.tensor_pwc(np.array(durations),
delta_segments),
operator=H_delta,
),
qctrl.operations.sparse_pwc_operator(
signal=qctrl.operations.tensor_pwc(np.array([duration]),
fixed_segment),
operator=H_fixed,
),
])
sample_times = np.linspace(0, duration, 200)
# Evolve the initial state
evolved_states = qctrl.operations.state_evolution_pwc(
initial_state=initial_state,
hamiltonian=hamiltonian,
krylov_subspace_dimension=krylov_subspace_dimension,
sample_times=sample_times,
name="evolved_states",
)
states = qctrl.functions.calculate_graph(
graph=graph,
output_node_names=["evolved_states"],
)
# Data to plot
idx_even = sum([2**n for n in range(0, qubit_count, 2)])
idx_odd = sum([2**n for n in range(1, qubit_count, 2)])
densities_t = abs(np.squeeze(states.output["evolved_states"]["value"]))**2
initial_density_t = densities_t[:, 0]
final_density_t = densities_t[:, idx_odd] + densities_t[:, idx_even]
target_density = np.abs(target_state)**2
# Style and label definitions
plt.style.use(get_qctrl_style())
bar_labels = [""] * 2**qubit_count
bar_labels[idx_odd] = "|" + "10" * int(qubit_count / 2) + ">"
bar_labels[idx_even] = "|" + "01" * int(qubit_count / 2) + ">"
def bar_plot(ax, density, title):
ax.bar(
np.arange(2**qubit_count),
density,
edgecolor="#680CE9",
color="#680CE94C",
tick_label=bar_labels,
linewidth=2,
)
ax.set_title(title, fontsize=14)
ax.set_ylabel("Population", fontsize=14)
ax.tick_params(labelsize=14)
gs = gridspec.GridSpec(2, 2, hspace=0.3)
fig = plt.figure()
fig.set_figheight(2 * 5)
fig.set_figwidth(15)
fig.suptitle("Optimized state preparation", fontsize=16, y=0.95)
# Plot optimized final state and target state
ax = fig.add_subplot(gs[0, 0])
bar_plot(ax, densities_t[-1], "Optimized final state")
ax = fig.add_subplot(gs[0, 1])
bar_plot(ax, target_density, "Target GHZ state")
# Plot time evolution of basis state population
ax = fig.add_subplot(gs[1, :])
ax.plot(
sample_times,
densities_t,
"#AAAAAA",
linewidth=0.5,
)
ax.plot(
sample_times,
initial_density_t,
"#FB00A5",
linewidth=1.5,
)
ax.plot(
sample_times,
final_density_t,
"#680CE9",
linewidth=2,
)
ax.set_xticks(np.linspace(0, 1e-6, 6))
ax.set_xticklabels(["0", "200 n", "400 n", "600 n", "800 n", "1 ยต"])
ax.tick_params(labelsize=14)
ax.set_xlabel("Time (s)", fontsize=14)
ax.set_ylabel("Population", fontsize=14)
ax.set_title(
"Basis states (gray), ground state (pink), and target state (purple)",
fontsize=14,
)
from scipy import interpolate
from scipy.optimize import curve_fit
import os
# Q-CTRL imports
from qctrl import Qctrl
# Starting a session with the API
qctrl = Qctrl(email=os.getenv('EMAIL'), password=os.getenv('PASSWORD'))
# Choose to run experiments or to use saved data
use_saved_data = False
# Plotting parameters
plt.style.use(get_qctrl_style())
prop_cycle = plt.rcParams["axes.prop_cycle"]
colors = prop_cycle.by_key()["color"]
markers = {"x": "x", "y": "s", "z": "o"}
lines = {"x": "--", "y": "-.", "z": "-"}
# Definition of operators and functions
sigma_z = np.array([[1.0, 0.0], [0.0, -1.0]], dtype=np.complex)
sigma_x = np.array([[0.0, 1.0], [1.0, 0.0]], dtype=np.complex)
sigma_y = np.array([[0.0, -1.0j], [1.0j, 0.0]], dtype=np.complex)
X90_gate = np.array([[1.0, -1j], [-1j, 1.0]], dtype=np.complex) / np.sqrt(2)
bloch_basis = ["x", "y", "z"]
def save_var(file_name, var):
# saves a single var to a file using jsonpickle
# In[2]:
# Define standard matrices
identity = np.array([[1.0, 0.0], [0.0, 1.0]], dtype=np.complex)
sigma_x = np.array([[0.0, 1.0], [1.0, 0.0]], dtype=np.complex)
sigma_y = np.array([[0.0, -1j], [1j, 0.0]], dtype=np.complex)
sigma_z = np.array([[1.0, 0.0], [0.0, -1.0]], dtype=np.complex)
sigma_m = np.array([[0.0, 1.0], [0.0, 0.0]], dtype=np.complex)
sigmas = [sigma_x, sigma_y, sigma_z]
sigma_names = ["X", "Y", "Z"]
not_gate = np.array([[0.0, -1.0], [1.0, 0.0]])
# Plotting and formatting methods
plt.style.use(qv.get_qctrl_style())
def plot_simulation_trajectories(figure, times, coherent_samples, noisy_trajectories):
ideal_bloch_sphere_coords = np.array(
[
[
np.real(
np.dot(
sample.state_vector.conj(),
np.matmul(sigma, sample.state_vector),
)
)
for sigma in sigmas
]
for sample in coherent_samples