def _setup_ss(**kwargs):
nk.random.seed(SEED)
np.random.seed(SEED)
hi, lind = _setup_system()
ma = nk.machine.density_matrix.RbmSpin(hilbert=hi, alpha=1)
ma.init_random_parameters(sigma=0.01, seed=SEED)
sa = nk.sampler.MetropolisLocal(machine=ma)
sa_obs = nk.sampler.MetropolisLocal(machine=ma.diagonal())
op = nk.optimizer.Sgd(ma, learning_rate=0.1)
if "sr" in kwargs:
sr = nk.optimizer.SR(ma, **kwargs["sr"])
kwargs["sr"] = sr
ss = nk.SteadyState(lindblad=lind,
sampler=sa,
optimizer=op,
sampler_obs=sa_obs,
**kwargs)
return ma, ss
def _setup_ss(dtype=np.float32, sr=True):
hi, lind = _setup_system()
ma = nk.models.NDM()
# sa = nk.sampler.ExactSampler(hilbert=nk.hilbert.DoubledHilber(hi))
sa = nk.sampler.MetropolisLocal(hilbert=nk.hilbert.DoubledHilbert(hi))
sa_obs = nk.sampler.MetropolisLocal(hilbert=hi)
vs = nk.vqs.MCMixedState(sa,
ma,
sampler_diag=sa_obs,
n_samples=1000,
seed=SEED)
op = nk.optimizer.Sgd(learning_rate=0.05)
if sr:
sr_config = nk.optimizer.SR()
else:
sr_config = None
driver = nk.SteadyState(lind,
op,
variational_state=vs,
preconditioner=sr_config)
return lind, vs, driver
def test_steadystate_construction_vstate():
lind, vs, driver = _setup_ss()
sa = vs.sampler
op = nk.optimizer.Sgd(learning_rate=0.05)
driver = nk.SteadyState(lind,
op,
sa,
nk.models.NDM(),
n_samples=1000,
seed=SEED)
driver.run(1)
assert driver.step_count == 1
with raises(TypeError):
ha2 = nk.operator.LocalOperator(lind.hilbert_physical)
driver = nk.SteadyState(ha2, op, variational_state=driver.state)
def test_steadystate_steadystate_legacy_api():
lind, vs, driver = _setup_ss()
op = driver.optimizer
vs = driver.state
sr_config = driver.preconditioner
with pytest.raises(ValueError):
driver = nk.SteadyState(lind,
op,
variational_state=vs,
sr=sr_config,
preconditioner=sr_config)
with pytest.warns(FutureWarning):
driver = nk.SteadyState(lind,
op,
variational_state=vs,
sr=sr_config,
sr_restart=True)
assert driver.preconditioner == sr_config
def _setup_ss(**kwargs):
nk.random.seed(SEED)
np.random.seed(SEED)
g = nk.graph.Hypercube(length=L, n_dim=1)
hi = nk.hilbert.Spin(s=0.5, graph=g)
# ma = nk.machine.density_matrix.RbmSpin(hilbert=hi, alpha=1)
ma = nk.machine.density_matrix.RbmSpin(hilbert=hi, alpha=1)
ma.init_random_parameters(sigma=0.01, seed=SEED)
ha = nk.operator.LocalOperator(hi)
j_ops = []
for i in range(L):
ha += (0.3 / 2.0) * nk.operator.LocalOperator(hi, sx, [i])
ha += (2.0 / 4.0) * nk.operator.LocalOperator(hi, np.kron(sz, sz),
[i, (i + 1) % L])
j_ops.append(nk.operator.LocalOperator(hi, sigmam, [i]))
# Create the liouvillian
lind = nk.operator.LocalLiouvillian(ha, j_ops)
sa = nk.sampler.MetropolisLocal(machine=ma)
sa_obs = nk.sampler.MetropolisLocal(machine=ma.diagonal())
op = nk.optimizer.Sgd(ma, learning_rate=0.1)
if "sr" in kwargs:
sr = nk.optimizer.SR(ma, **kwargs["sr"])
kwargs["sr"] = sr
ss = nk.SteadyState(lindblad=lind,
sampler=sa,
optimizer=op,
sampler_obs=sa_obs,
**kwargs)
return ma, ss
def _setup_ss(**kwargs):
nk.utils.seed(SEED)
g = nk.graph.Hypercube(length=L, n_dim=1)
hi = nk.hilbert.Spin(s=0.5, graph=g)
ma = nk.machine.NdmSpinPhase(hilbert=hi, alpha=1, beta=1)
ma.init_random_parameters(sigma=0.01)
ha = nk.operator.LocalOperator(hi)
obs_sx = nk.operator.LocalOperator(hi)
j_ops = []
for i in range(L):
ha += (0.3 / 2.0) * nk.operator.LocalOperator(hi, sx, [i])
ha += (2.0 / 4.0) * nk.operator.LocalOperator(hi, np.kron(sz, sz),
[i, (i + 1) % L])
j_ops.append(nk.operator.LocalOperator(hi, sigmam, [i]))
obs_sx += nk.operator.LocalOperator(hi, sx, [i])
# Create the liouvillian
lind = nk.operator.LocalLiouvillian(ha, j_ops)
sa = nk.sampler.MetropolisLocal(machine=ma)
sa_obs = nk.sampler.MetropolisLocal(machine=ma.diagonal())
op = nk.optimizer.Sgd(learning_rate=0.1)
ss = nk.SteadyState(lindblad=lind,
sampler=sa,
optimizer=op,
sampler_obs=sa_obs,
**kwargs)
# Add custom observable
ss.add_observable(obs_sx, "SigmaX")
return ma, ss
for i in range(L):
ha += (gp / 2.0) * nk.operator.spin.sigmax(hi, i)
ha += ((Vp / 4.0) * nk.operator.spin.sigmaz(hi, i) *
nk.operator.spin.sigmaz(hi, (i + 1) % L))
# sigma_{-} dissipation on every site
j_ops.append(nk.operator.spin.sigmam(hi, i))
obs_sx += nk.operator.spin.sigmax(hi, i)
obs_sy += nk.operator.spin.sigmay(hi, i)
obs_sz += nk.operator.spin.sigmaz(hi, i)
# Create the liouvillian
lind = nk.operator.LocalLiouvillian(ha, j_ops)
ma = nk.models.NDM(beta=1, )
# Metropolis Local Sampling
sa = nk.sampler.MetropolisLocal(lind.hilbert)
# Optimizer
op = nk.optimizer.Sgd(0.01)
sr = nk.optimizer.SR(diag_shift=0.01)
vs = nk.vqs.MCMixedState(sa, ma, n_samples=2000, n_samples_diag=500)
vs.init_parameters(nk.nn.initializers.normal(stddev=0.01))
ss = nk.SteadyState(lind, op, variational_state=vs, preconditioner=sr)
obs = {"Sx": obs_sx, "Sy": obs_sy, "Sz": obs_sz}
out = ss.run(n_iter=300, out="test", obs=obs)
ha += (Vp / 4.0) * nk.operator.LocalOperator(hi, np.kron(sz, sz),
[i, (i + 1) % L])
# sigma_{-} dissipation on every site
j_ops.append(nk.operator.LocalOperator(hi, sigmam, [i]))
obs_sx += nk.operator.LocalOperator(hi, sx, [i])
obs_sy += nk.operator.LocalOperator(hi, sy, [i])
obs_sz += nk.operator.LocalOperator(hi, sz, [i])
# Create the liouvillian
lind = nk.operator.LocalLiouvillian(ha, j_ops)
# RBM Spin Machine
ma = nk.machine.NdmSpinPhase(hilbert=hi, alpha=1, beta=1)
ma.init_random_parameters(seed=1234, sigma=0.001)
# Metropolis Local Sampling
sa = nk.sampler.MetropolisLocal(machine=ma)
# Sampler for the diagonal of the density matrix, to compute observables
sa_obs = nk.sampler.MetropolisLocal(machine=ma.diagonal())
# Optimizer
op = nk.optimizer.Sgd(ma, 0.01)
sr = nk.optimizer.SR(ma, diag_shift=0.01, use_iterative=True)
ss = nk.SteadyState(lind, sa, op, 2000, sampler_obs=sa_obs, n_samples_obs=500)
obs = {"Sx": obs_sx, "Sy": obs_sy, "Sz": obs_sz}
ss.run(n_iter=200, out="test", obs=obs)
for i in range(L):
ha += (gp / 2.0) * nk.operator.spin.sigmax(hi, i)
ha += ((Vp / 4.0) * nk.operator.spin.sigmaz(hi, i) *
nk.operator.spin.sigmaz(hi, (i + 1) % L))
# sigma_{-} dissipation on every site
j_ops.append(nk.operator.spin.sigmam(hi, i))
obs_sx += nk.operator.spin.sigmax(hi, i)
obs_sy += nk.operator.spin.sigmay(hi, i)
obs_sz += nk.operator.spin.sigmaz(hi, i)
# Create the liouvillian
lind = nk.operator.LocalLiouvillian(ha, j_ops)
ma = nk.models.NDM(beta=1, )
# Metropolis Local Sampling
sa = nk.sampler.MetropolisLocal(lind.hilbert)
# Optimizer
op = nk.optimizer.Sgd(0.01)
sr = nk.optimizer.SR(diag_shift=0.01)
vs = nk.variational.MCMixedState(sa, ma, n_samples=2000, n_samples_diag=500)
vs.init_parameters(nk.nn.initializers.normal(stddev=0.01))
ss = nk.SteadyState(lind, op, variational_state=vs, sr=sr)
obs = {"Sx": obs_sx, "Sy": obs_sy, "Sz": obs_sz}
out = ss.run(n_iter=300, out="test", obs=obs)