def plot_basis_state_populations_3d(e_qs: EvolvingQubitSystem):
if e_qs.solve_result is None:
e_qs.solve()
states = get_states(e_qs.N)
times = []
heights = []
xs = []
for i, _solve_result_state in enumerate(e_qs.solve_result.states):
plt.ylim(0, 1)
plt.grid(axis='y')
solve_result_state_populations = np.abs(_solve_result_state.data.toarray().flatten()) ** 2
for _x, state in enumerate(states):
state_product_basis_index = get_product_basis_states_index(state)
basis_state_population = solve_result_state_populations[state_product_basis_index]
times.append(e_qs.solve_result.times[i])
xs.append(_x)
heights.append(basis_state_population)
fig = plt.figure(figsize=(15, 8))
ax = fig.add_subplot(111, projection="3d")
dt = e_qs.solve_result.times[1] - e_qs.solve_result.times[0]
ax.bar3d(xs, times, z=0, dx=0.8, dy=dt, dz=heights)
labels = [get_label_from_state(state) for state in states]
x = np.arange(len(labels))
plt.xticks(x, labels)
ax.yaxis.set_major_formatter(ticker.EngFormatter('s'))
plt.ylabel('Time')
plt.show()
def solve_and_print_stats(e_qs: EvolvingQubitSystem):
import time
start_time = time.time()
e_qs.solve()
print(f"Solved in {time.time() - start_time:.2f}s")
fidelity_with_ghz = e_qs.get_fidelity_with("ghz")
fidelity_with_ghz_asymmetric = e_qs.get_fidelity_with("ghz_antisymmetric")
print(f"fidelity with GHZ: {fidelity_with_ghz:.4f} (with antisymmetric: {fidelity_with_ghz_asymmetric:.4f})")
fidelity_with_ground = e_qs.get_fidelity_with("ground")
fidelity_with_excited = e_qs.get_fidelity_with("excited")
superposition_probability = fidelity_with_ground + fidelity_with_excited
print(
f"superposition probability: {superposition_probability:.4f} (g: {fidelity_with_ground:.4f}, e: {fidelity_with_excited:.4f})")
e_qs.plot(with_antisymmetric_ghz=True)
def solve_and_print_stats(e_qs: EvolvingQubitSystem):
e_qs.solve()
fidelity_with_ghz = e_qs.get_fidelity_with("ghz")
fidelity_with_ghz_asymmetric = e_qs.get_fidelity_with("ghz_antisymmetric")
print(
f"fidelity with GHZ: {fidelity_with_ghz:.4f} (with antisymmetric: {fidelity_with_ghz_asymmetric:.4f})"
)
fidelity_with_ground = e_qs.get_fidelity_with("ground")
fidelity_with_excited = e_qs.get_fidelity_with("excited")
superposition_probability = fidelity_with_ground + fidelity_with_excited
print(
f"superposition probability: {superposition_probability:.4f} (g: {fidelity_with_ground:.4f}, e: {fidelity_with_excited:.4f})"
)
e_qs.plot(with_antisymmetric_ghz=True,
savefig_name="evolving_fidelities.png",
show=True)
def rescale_evolving_qubit_system():
t = 1.1e-6
N = 4
e_qs = EvolvingQubitSystem(
N=N,
V=paper_data.V,
geometry=RegularLattice1D(),
Omega=paper_data.get_hamiltonian_coeff_fn(paper_data.Omega, N),
Delta=paper_data.get_hamiltonian_coeff_fn(paper_data.Delta, N),
t_list=np.linspace(0, t, 100),
ghz_state=AlternatingGHZState(N))
e_qs.solve()
# e_qs.plot()
print(e_qs.get_fidelity_with("ghz"))
max_Omega = 50e6
e_qs = EvolvingQubitSystem(
N=N,
V=paper_data.V / max_Omega,
geometry=RegularLattice1D(),
Omega=paper_data.get_hamiltonian_coeff_fn(
{
k: {_k * max_Omega: _v / max_Omega
for _k, _v in v.items()}
for k, v in paper_data.Omega.items()
}, N),
Delta=paper_data.get_hamiltonian_coeff_fn(
{
k: {_k * max_Omega: _v / max_Omega
for _k, _v in v.items()}
for k, v in paper_data.Delta.items()
}, N),
t_list=np.linspace(0, t * max_Omega, 100),
ghz_state=AlternatingGHZState(N))
e_qs.solve()
e_qs.plot()
print(e_qs.get_fidelity_with("ghz"))
def solve_and_print_stats(e_qs: EvolvingQubitSystem, **kwargs):
import time
start_time = time.time()
e_qs.solve()
print(f"Solved in {time.time() - start_time:.2f}s")
fidelity_with_ghz = e_qs.get_fidelity_with("ghz")
fidelity_with_ghz_asymmetric = e_qs.get_fidelity_with("ghz_antisymmetric")
print(
f"fidelity with GHZ: {fidelity_with_ghz:.4f} (with antisymmetric: {fidelity_with_ghz_asymmetric:.4f})"
)
fidelity_with_ground = e_qs.get_fidelity_with("ground")
fidelity_with_excited = e_qs.get_fidelity_with("excited")
superposition_probability = fidelity_with_ground + fidelity_with_excited
print(
f"superposition probability: {superposition_probability:.4f} (g: {fidelity_with_ground:.4f}, e: {fidelity_with_excited:.4f})"
)
e_qs.plot(with_antisymmetric_ghz=True,
**kwargs,
fig_kwargs={'figsize': (6, 4)},
plot_titles=False,
plot_others_as_sum=True)
def plot_basis_state_populations_2d(e_qs: EvolvingQubitSystem, log=False, log_limit=1e-6):
if e_qs.solve_result is None:
e_qs.solve()
quartile_index = int(len(e_qs.t_list) / 4)
indices = [0, quartile_index, quartile_index * 2, quartile_index * 3, -1]
states = get_states(e_qs.N)
labels = [get_label_from_state(state) for state in states]
x = np.arange(len(labels))
fig, axs = plt.subplots(len(indices) , 1, sharex='all', figsize=(10, 8))
for _i, i in enumerate(indices):
ax = axs[_i]
if not log:
ax.set_ylim(0, 1)
else:
ax.set_ylim(log_limit, 1)
ax.set_yscale('log', basey=10)
ax.grid(axis='y')
ax.set_ylabel(f"{e_qs.solve_result.times[i]:.2e}s")
basis_state_populations = []
_solve_result_state = e_qs.solve_result.states[i]
solve_result_state_populations = np.abs(_solve_result_state.data.toarray().flatten()) ** 2
for state in states:
state_product_basis_index = get_product_basis_states_index(state)
basis_state_population = solve_result_state_populations[state_product_basis_index]
basis_state_populations.append(basis_state_population)
if log:
basis_state_population += log_limit
ax.bar(x=x, height=basis_state_populations)
plt.xticks(x, labels)
plt.tight_layout()
plt.show()