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density_matrix.py
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/
density_matrix.py
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# Copyright 2019 PIQuIL - All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
import torch
from torch.nn import functional as F
from qucumber import _warn_on_missing_gpu
from qucumber.utils import cplx, unitaries
from qucumber.rbm import PurificationRBM
from .neural_state import NeuralStateBase
class DensityMatrix(NeuralStateBase):
r"""
:param num_visible: The number of visible units, i.e. the size of the system
:type num_visible: int
:param num_hidden: The number of units in the hidden layer
:type num_hidden: int
:param num_aux: The number of units in the purification layer
:type num_aux: int
:param unitary_dict: A dictionary associating bases with their unitary rotations
:type unitary_dict: dict[str, torch.Tensor]
:param gpu: Whether to perform computations on the default gpu.
:type gpu: bool
"""
_rbm_am = None
_rbm_ph = None
_device = None
def __init__(
self,
num_visible,
num_hidden=None,
num_aux=None,
unitary_dict=None,
gpu=False,
module=None,
):
if gpu and torch.cuda.is_available():
warnings.warn(
"Using DensityMatrix on GPU is not recommended due to poor performance compared to CPU.",
ResourceWarning,
2,
)
self.device = torch.device("cuda")
else:
self.device = torch.device("cpu")
if module is None:
self.rbm_am = PurificationRBM(num_visible, num_hidden, num_aux, gpu=gpu)
self.rbm_ph = PurificationRBM(num_visible, num_hidden, num_aux, gpu=gpu)
else:
_warn_on_missing_gpu(gpu)
self.rbm_am = module.to(self.device)
self.rbm_am.device = self.device
self.rbm_ph = module.to(self.device).clone()
self.rbm_ph.device = self.device
self.num_visible = self.rbm_am.num_visible
self.num_hidden = self.rbm_am.num_hidden
self.num_aux = self.rbm_am.num_aux
self.device = self.rbm_am.device
self.unitary_dict = unitary_dict if unitary_dict else unitaries.create_dict()
self.unitary_dict = {
k: v.to(device=self.device) for k, v in self.unitary_dict.items()
}
@property
def networks(self):
return ["rbm_am", "rbm_ph"]
@property
def rbm_am(self):
return self._rbm_am
@rbm_am.setter
def rbm_am(self, new_val):
self._rbm_am = new_val
@property
def rbm_ph(self):
"""RBM used to learn the wavefunction phase."""
return self._rbm_ph
@rbm_ph.setter
def rbm_ph(self, new_val):
self._rbm_ph = new_val
@property
def device(self):
return self._device
@device.setter
def device(self, new_val):
self._device = new_val
def pi(self, v, vp, expand=True):
r"""Calculates elements of the :math:`\Pi` matrix.
If `expand` is `True`, will return a complex matrix
:math:`A_{ij} = \langle\sigma_i|\Pi|\sigma'_j\rangle`.
Otherwise will return a complex vector
:math:`A_{i} = \langle\sigma_i|\Pi|\sigma'_i\rangle`.
:param v: A batch of visible states, :math:`\sigma`.
:type v: torch.Tensor
:param vp: The other batch of visible state, :math:`\sigma'`.
:type vp: torch.Tensor
:param expand: Whether to return a matrix (`True`) or a vector (`False`).
:type expand: bool
:returns: The matrix elements given by :math:`\langle\sigma|\Pi|\sigma'\rangle`
:rtype: torch.Tensor
"""
m_am = F.linear(v, self.rbm_am.weights_U, self.rbm_am.aux_bias)
mp_am = F.linear(vp, self.rbm_am.weights_U, self.rbm_am.aux_bias)
m_ph = F.linear(v, self.rbm_ph.weights_U)
mp_ph = F.linear(vp, self.rbm_ph.weights_U)
if expand and v.dim() >= 2:
m_am = m_am.unsqueeze_(1)
m_ph = m_ph.unsqueeze_(1)
if expand and vp.dim() >= 2:
mp_am = mp_am.unsqueeze_(0)
mp_ph = mp_ph.unsqueeze_(0)
exp_arg = (m_am + mp_am) / 2
phase = (m_ph - mp_ph) / 2
real = (
(1 + 2 * exp_arg.exp() * phase.cos() + (2 * exp_arg).exp())
.sqrt()
.log()
.sum(-1)
)
imag = torch.atan2(
(exp_arg.exp() * phase.sin()), (1 + exp_arg.exp() * phase.cos())
).sum(-1)
return cplx.make_complex(real, imag)
def pi_grad(self, v, vp, phase=False, expand=False):
r"""Calculates the gradient of the :math:`\Pi` matrix with
respect to the amplitude RBM parameters for two input states
:param v: One of the visible states, :math:`\sigma`
:type v: torch.Tensor
:param vp: The other visible state, :math`\sigma'`
:type vp: torch.Tensor
:param phase: Whether to compute the gradients for the phase RBM (`True`)
or the amplitude RBM (`False`)
:type phase: bool
:returns: The matrix element of the gradient given by
:math:`\langle\sigma|\nabla_\lambda\Pi|\sigma'\rangle`
:rtype: torch.Tensor
"""
unsqueezed = v.dim() < 2 or vp.dim() < 2
v = (v.unsqueeze(0) if v.dim() < 2 else v).to(self.rbm_am.weights_W)
vp = (vp.unsqueeze(0) if vp.dim() < 2 else vp).to(self.rbm_am.weights_W)
if expand:
arg_real = 0.5 * (
F.linear(v, self.rbm_am.weights_U, self.rbm_am.aux_bias).unsqueeze_(1)
+ F.linear(vp, self.rbm_am.weights_U, self.rbm_am.aux_bias).unsqueeze_(
0
)
)
arg_imag = 0.5 * (
F.linear(v, self.rbm_ph.weights_U).unsqueeze_(1)
- F.linear(vp, self.rbm_ph.weights_U).unsqueeze_(0)
)
else:
arg_real = self.rbm_am.mixing_term(v + vp)
arg_imag = self.rbm_ph.mixing_term(v - vp)
sig = cplx.sigmoid(arg_real, arg_imag)
batch_sizes = (
(v.shape[0], vp.shape[0], *v.shape[1:-1]) if expand else (*v.shape[:-1],)
)
W_grad = torch.zeros_like(self.rbm_am.weights_W).expand(*batch_sizes, -1, -1)
vb_grad = torch.zeros_like(self.rbm_am.visible_bias).expand(*batch_sizes, -1)
hb_grad = torch.zeros_like(self.rbm_am.hidden_bias).expand(*batch_sizes, -1)
if phase:
temp = (v.unsqueeze(1) - vp.unsqueeze(0)) if expand else (v - vp)
sig = cplx.scalar_mult(sig, cplx.I)
ab_grad_real = torch.zeros_like(self.rbm_ph.aux_bias).expand(
*batch_sizes, -1
)
ab_grad_imag = ab_grad_real.clone()
else:
temp = (v.unsqueeze(1) + vp.unsqueeze(0)) if expand else (v + vp)
ab_grad_real = cplx.real(sig)
ab_grad_imag = cplx.imag(sig)
U_grad = 0.5 * torch.einsum("c...j,...k->c...jk", sig, temp)
U_grad_real = cplx.real(U_grad)
U_grad_imag = cplx.imag(U_grad)
vec_real = [
W_grad.view(*batch_sizes, -1),
U_grad_real.view(*batch_sizes, -1),
vb_grad,
hb_grad,
ab_grad_real,
]
vec_imag = [
W_grad.view(*batch_sizes, -1).clone(),
U_grad_imag.view(*batch_sizes, -1),
vb_grad.clone(),
hb_grad.clone(),
ab_grad_imag,
]
if unsqueezed and not expand:
vec_real = [grad.squeeze_(0) for grad in vec_real]
vec_imag = [grad.squeeze_(0) for grad in vec_imag]
return cplx.make_complex(
torch.cat(vec_real, dim=-1), torch.cat(vec_imag, dim=-1)
)
def rho(self, v, vp=None, expand=True):
r"""Computes the matrix elements of the (unnormalized) density matrix.
If `expand` is `True`, will return a complex matrix
:math:`A_{ij} = \langle\sigma_i|\widetilde{\rho}|\sigma'_j\rangle`.
Otherwise will return a complex vector
:math:`A_{i} = \langle\sigma_i|\widetilde{\rho}|\sigma'_i\rangle`.
:param v: One of the visible states, :math:`\sigma`.
:type v: torch.Tensor
:param vp: The other visible state, :math:`\sigma'`.
If `None`, will be set to `v`.
:type vp: torch.Tensor
:param expand: Whether to return a matrix (`True`) or a vector (`False`).
:type expand: bool
:returns: The elements of the current density matrix
:math:`\langle\sigma|\widetilde{\rho}|\sigma'\rangle`
:rtype: torch.Tensor
"""
if expand is False and vp is None:
return cplx.make_complex(self.probability(v))
elif vp is None:
vp = v
pi_ = self.pi(v, vp, expand=expand)
amp = (self.rbm_am.gamma(v, vp, eta=+1, expand=expand) + cplx.real(pi_)).exp()
phase = self.rbm_ph.gamma(v, vp, eta=-1, expand=expand) + cplx.imag(pi_)
return cplx.make_complex(amp * phase.cos(), amp * phase.sin())
def importance_sampling_numerator(self, vp, v):
return self.rho(vp, v, expand=False)
def importance_sampling_denominator(self, v):
return cplx.make_complex(self.probability(v))
def rotated_gradient(self, basis, sample):
r"""Computes the gradients rotated into the measurement basis
:param basis: The bases in which the measurement is made
:type basis: numpy.ndarray
:param sample: The measurement (either 0 or 1)
:type sample: torch.Tensor
:returns: A list of two tensors, representing the rotated gradients
of the amplitude and phase RBMs
:rtype: list[torch.Tensor, torch.Tensor]
"""
UrhoU, UrhoU_v, v = unitaries.rotate_rho_probs(
self, basis, sample, include_extras=True
)
inv_UrhoU = 1 / (UrhoU + 1e-8) # avoid dividing by zero
raw_grads = [self.am_grads(v), self.ph_grads(v)]
rotated_grad = [
-cplx.einsum("ijb,ijbg->bg", UrhoU_v, g, imag_part=False) for g in raw_grads
]
return [torch.einsum("b,bg->g", inv_UrhoU, g) for g in rotated_grad]
def am_grads(self, v):
r"""Computes the gradients of the amplitude RBM for given input states
:param v: The first input state, :math:`\sigma`
:type v: torch.Tensor
:returns: The gradients of all amplitude RBM parameters
:rtype: torch.Tensor
"""
return self.rbm_am.gamma_grad(v, v, eta=+1, expand=True) + self.pi_grad(
v, v, phase=False, expand=True
)
def ph_grads(self, v):
r"""Computes the gradients of the phase RBM for given input states
:param v: The first input state, :math:`\sigma`
:type v: torch.Tensor
:returns: The gradients of all phase RBM parameters
:rtype: torch.Tensor
"""
return cplx.scalar_mult( # need to multiply Gamma- by i
self.rbm_ph.gamma_grad(v, v, eta=-1, expand=True), cplx.I
) + self.pi_grad(v, v, phase=True, expand=True)
def fit(
self,
data,
epochs=100,
pos_batch_size=100,
neg_batch_size=None,
k=1,
lr=1,
input_bases=None,
progbar=False,
starting_epoch=1,
time=False,
callbacks=None,
optimizer=torch.optim.SGD,
optimizer_args=None,
scheduler=None,
scheduler_args=None,
**kwargs,
):
if input_bases is None:
raise ValueError("input_bases must be provided to train a DensityMatrix!")
else:
super().fit(
data=data,
epochs=epochs,
pos_batch_size=pos_batch_size,
neg_batch_size=neg_batch_size,
k=k,
lr=lr,
input_bases=input_bases,
progbar=progbar,
starting_epoch=starting_epoch,
time=time,
callbacks=callbacks,
optimizer=optimizer,
optimizer_args=optimizer_args,
scheduler=scheduler,
scheduler_args=scheduler_args,
**kwargs,
)
@staticmethod
def autoload(location, gpu=False):
state_dict = torch.load(location)
nn_state = DensityMatrix(
unitary_dict=state_dict["unitary_dict"],
num_visible=len(state_dict["rbm_am"]["visible_bias"]),
num_hidden=len(state_dict["rbm_am"]["hidden_bias"]),
num_aux=len(state_dict["rbm_am"]["aux_bias"]),
gpu=gpu,
)
nn_state.load(location)
return nn_state