def evolve_trotter(psi,
H,
step_size,
num_steps,
euclidean=False,
callback=None):
"""Evolve an initial wavefunction psi using a trotter decomposition of H.
If the evolution is euclidean, the wavefunction will be normalized after
each step.
Args:
psi: An `N`-dimensional tensor representing the initial wavefunction.
H: A list of `N-1` tensors representing nearest-neighbor operators.
step_size: The trotter step size.
num_steps: The number of trotter steps to take.
euclidean: If `True`, evolve in Euclidean (imaginary) time.
callback: Optional callback function for monitoring the evolution.
Returns:
psi_t: The final wavefunction.
t: The final time.
"""
num_sites = len(psi.shape)
layers = trotter_prepare_gates(H, step_size, num_sites, euclidean)
return _evolve_trotter_gates(psi,
layers,
step_size,
num_steps,
euclidean=euclidean,
callback=callback)
def evolve_trotter_defun(psi,
H,
step_size,
num_steps,
euclidean=False,
callback=None,
batch_size=1):
"""Evolve an initial wavefunction psi using a trotter decomposition of H.
If the evolution is euclidean, the wavefunction will be normalized after
each step.
In this version, `batch_size` steps are "compiled" to a computational graph
using `defun`, which greatly decreases overhead.
Args:
psi: An `N`-dimensional tensor representing the initial wavefunction.
H: A list of `N-1` tensors representing nearest-neighbor operators.
step_size: The trotter step size.
num_steps: The number of trotter steps to take.
euclidean: If `True`, evolve in Euclidean (imaginary) time.
callback: Optional callback function for monitoring the evolution.
batch_size: The number of steps to unroll in the computational graph.
Returns:
psi_t: The final wavefunction.
t: The final time.
"""
n_batches, rem = divmod(num_steps, batch_size)
step_size = tf.cast(step_size, psi.dtype)
num_sites = len(psi.shape)
layers = trotter_prepare_gates(H, step_size, num_sites, euclidean)
t = 0.0
for i in range(n_batches):
psi, t_b = _evolve_trotter_gates_defun(psi,
layers,
step_size,
batch_size,
euclidean=euclidean,
callback=None)
t += t_b
if callback is not None:
callback(psi, t, (i + 1) * batch_size - 1)
if rem > 0:
psi, t_b = _evolve_trotter_gates_defun(psi,
layers,
step_size,
rem,
euclidean=euclidean,
callback=None)
t += t_b
return psi, t
def evolve_trotter(psi, H, step_size, num_steps, euclidean=False, callback=None):
"""Evolve an initial state psi using a trotter decomposition of H.
If the evolution is euclidean, the wavefunction will be normalized after
each step.
"""
num_sites = len(psi.shape)
layers = trotter_prepare_gates(H, step_size, num_sites, euclidean)
return _evolve_trotter_gates(
psi, layers, step_size, num_steps,
euclidean=euclidean,
callback=callback)
def evolve_trotter(psi,
H,
step_size,
num_steps,
euclidean=False,
callback=None):
"""Evolve an initial state psi using a trotter decomposition of H.
If the evolution is euclidean, the wavefunction will be normalized after
each step.
"""
num_sites = len(psi.shape)
layers = trotter_prepare_gates(H, step_size, num_sites, euclidean)
return _evolve_trotter_gates(psi,
layers,
step_size,
num_steps,
euclidean=euclidean,
callback=callback)
def evolve_trotter_defun(psi,
H,
step_size,
num_steps,
euclidean=False,
callback=None,
batch_size=1):
"""Evolve an initial state psi using a trotter decomposition of H.
If the evolution is euclidean, the wavefunction will be normalized after
each step.
In this version, `batch_size` steps are "compiled" to a computational graph
using `defun`, which greatly decreases overhead.
"""
n_batches, rem = divmod(num_steps, batch_size)
step_size = tf.cast(step_size, psi.dtype)
num_sites = len(psi.shape)
layers = trotter_prepare_gates(H, step_size, num_sites, euclidean)
t = 0.0
for i in range(n_batches):
psi, t_b = _evolve_trotter_gates_defun(psi,
layers,
step_size,
batch_size,
euclidean=euclidean,
callback=None)
t += t_b
if callback is not None:
callback(psi, t, (i + 1) * batch_size - 1)
if rem > 0:
psi, t_b = _evolve_trotter_gates_defun(psi,
layers,
step_size,
rem,
euclidean=euclidean,
callback=None)
t += t_b
return psi, t
def evolve_trotter_defun(
psi, H, step_size, num_steps,
euclidean=False,
callback=None,
batch_size=1):
"""Evolve an initial state psi using a trotter decomposition of H.
If the evolution is euclidean, the wavefunction will be normalized after
each step.
In this version, `batch_size` steps are "compiled" to a computational graph
using `defun`, which greatly decreases overhead.
"""
n_batches, rem = divmod(num_steps, batch_size)
step_size = tf.cast(step_size, psi.dtype)
num_sites = len(psi.shape)
layers = trotter_prepare_gates(H, step_size, num_sites, euclidean)
t = 0.0
for i in range(n_batches):
psi, t_b = _evolve_trotter_gates_defun(
psi, layers, step_size, batch_size,
euclidean=euclidean,
callback=None)
t += t_b
if callback is not None:
callback(psi, t, (i+1) * batch_size - 1)
if rem > 0:
psi, t_b = _evolve_trotter_gates_defun(
psi, layers, step_size, rem,
euclidean=euclidean,
callback=None)
t += t_b
return psi, t
