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training_statistics.py
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/
training_statistics.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 torch
from torch.distributions.utils import probs_to_logits
import numpy as np
from scipy.linalg import sqrtm
from qucumber.nn_states import WaveFunctionBase
from qucumber.utils import cplx, deprecated_kwarg
from qucumber.utils.unitaries import rotate_psi, rotate_psi_inner_prod, rotate_rho_probs
@deprecated_kwarg(target_psi="target", target_rho="target")
def fidelity(nn_state, target, space=None, **kwargs):
r"""Calculates the square of the overlap (fidelity) between the reconstructed
state and the true state (both in the computational basis).
.. math::
F = \vert \langle \psi_{RBM} \vert \psi_{target} \rangle \vert ^2
= \left( \tr \lbrack \sqrt{ \sqrt{\rho_{RBM}} \rho_{target} \sqrt{\rho_{RBM}} } \rbrack \right) ^ 2
:param nn_state: The neural network state.
:type nn_state: qucumber.nn_states.NeuralStateBase
:param target: The true state of the system.
:type target: torch.Tensor
:param space: The basis elements of the Hilbert space of the system :math:`\mathcal{H}`.
The ordering of the basis elements must match with the ordering of the
coefficients given in `target`. If `None`, will generate them using
the provided `nn_state`.
:type space: torch.Tensor
:param \**kwargs: Extra keyword arguments that may be passed. Will be ignored.
:returns: The fidelity.
:rtype: float
"""
space = space if space is not None else nn_state.generate_hilbert_space()
Z = nn_state.normalization(space)
target = target.to(nn_state.device)
if isinstance(nn_state, WaveFunctionBase):
assert target.dim() == 2, "target must be a complex vector!"
psi = nn_state.psi(space) / Z.sqrt()
F = cplx.inner_prod(target, psi)
return cplx.absolute_value(F).pow_(2).item()
else:
assert target.dim() == 3, "target must be a complex matrix!"
rho = nn_state.rho(space, space) / Z
rho_rbm_ = cplx.numpy(rho)
target_ = cplx.numpy(target)
sqrt_rho_rbm = sqrtm(rho_rbm_)
prod = np.matmul(sqrt_rho_rbm, np.matmul(target_, sqrt_rho_rbm))
# Instead of sqrt'ing then taking the trace, we compute the eigenvals,
# sqrt those, and then sum them up. This is a bit more efficient.
eigvals = np.linalg.eigvals(prod).real # imaginary parts should be zero
eigvals = np.abs(eigvals)
trace = np.sum(np.sqrt(eigvals))
return trace ** 2
def NLL(nn_state, samples, space=None, sample_bases=None, **kwargs):
r"""A function for calculating the negative log-likelihood (NLL).
:param nn_state: The neural network state.
:type nn_state: qucumber.nn_states.NeuralStateBase
:param samples: Samples to compute the NLL on.
:type samples: torch.Tensor
:param space: The basis elements of the Hilbert space of the system :math:`\mathcal{H}`.
If `None`, will generate them using the provided `nn_state`.
:type space: torch.Tensor
:param sample_bases: An array of bases where measurements were taken.
:type sample_bases: numpy.ndarray
:param \**kwargs: Extra keyword arguments that may be passed. Will be ignored.
:returns: The Negative Log-Likelihood.
:rtype: float
"""
space = space if space is not None else nn_state.generate_hilbert_space()
Z = nn_state.normalization(space)
if sample_bases is None:
nn_probs = nn_state.probability(samples, Z)
NLL_ = -torch.mean(probs_to_logits(nn_probs)).item()
return NLL_
else:
NLL_ = 0.0
unique_bases, indices = np.unique(sample_bases, axis=0, return_inverse=True)
indices = torch.Tensor(indices).to(samples)
for i in range(unique_bases.shape[0]):
basis = unique_bases[i, :]
rot_sites = np.where(basis != "Z")[0]
if rot_sites.size != 0:
if isinstance(nn_state, WaveFunctionBase):
Upsi = rotate_psi_inner_prod(
nn_state, basis, samples[indices == i, :]
)
nn_probs = (cplx.absolute_value(Upsi) ** 2) / Z
else:
nn_probs = (
rotate_rho_probs(nn_state, basis, samples[indices == i, :]) / Z
)
else:
nn_probs = nn_state.probability(samples[indices == i, :], Z)
NLL_ -= torch.sum(probs_to_logits(nn_probs))
return NLL_ / float(len(samples))
def _single_basis_KL(target_probs, nn_probs):
return torch.sum(target_probs * probs_to_logits(target_probs)) - torch.sum(
target_probs * probs_to_logits(nn_probs)
)
@deprecated_kwarg(target_psi="target", target_rho="target")
def KL(nn_state, target, space=None, bases=None, **kwargs):
r"""A function for calculating the KL divergence averaged over every given
basis.
.. math:: KL(P_{target} \vert P_{RBM}) = -\sum_{x \in \mathcal{H}} P_{target}(x)\log(\frac{P_{RBM}(x)}{P_{target}(x)})
:param nn_state: The neural network state.
:type nn_state: qucumber.nn_states.NeuralStateBase
:param target: The true state (wavefunction or density matrix) of the system.
Can be a dictionary with each value being the state
represented in a different basis, and the key identifying the basis.
:type target: torch.Tensor or dict(str, torch.Tensor)
:param space: The basis elements of the Hilbert space of the system :math:`\mathcal{H}`.
The ordering of the basis elements must match with the ordering of the
coefficients given in `target`. If `None`, will generate them using
the provided `nn_state`.
:type space: torch.Tensor
:param bases: An array of unique bases. If given, the KL divergence will be
computed for each basis and the average will be returned.
:type bases: numpy.ndarray
:param \**kwargs: Extra keyword arguments that may be passed. Will be ignored.
:returns: The KL divergence.
:rtype: float
"""
space = space if space is not None else nn_state.generate_hilbert_space()
Z = nn_state.normalization(space)
if isinstance(target, dict):
target = {k: v.to(nn_state.device) for k, v in target.items()}
if bases is None:
bases = list(target.keys())
else:
assert set(bases) == set(
target.keys()
), "Given bases must match the keys of the target_psi dictionary."
else:
target = target.to(nn_state.device)
KL = 0.0
if bases is None:
target_probs = cplx.absolute_value(target) ** 2
nn_probs = nn_state.probability(space, Z)
KL += _single_basis_KL(target_probs, nn_probs)
elif isinstance(nn_state, WaveFunctionBase):
for basis in bases:
if isinstance(target, dict):
target_psi_r = target[basis]
assert target_psi_r.dim() == 2, "target must be a complex vector!"
else:
assert target.dim() == 2, "target must be a complex vector!"
target_psi_r = rotate_psi(nn_state, basis, space, psi=target)
psi_r = rotate_psi(nn_state, basis, space)
nn_probs_r = (cplx.absolute_value(psi_r) ** 2) / Z
target_probs_r = cplx.absolute_value(target_psi_r) ** 2
KL += _single_basis_KL(target_probs_r, nn_probs_r)
KL /= float(len(bases))
else:
for basis in bases:
if isinstance(target, dict):
target_rho_r = target[basis]
assert target_rho_r.dim() == 3, "target must be a complex matrix!"
target_probs_r = torch.diagonal(cplx.real(target_rho_r))
else:
assert target.dim() == 3, "target must be a complex matrix!"
target_probs_r = rotate_rho_probs(nn_state, basis, space, rho=target)
rho_r = rotate_rho_probs(nn_state, basis, space)
nn_probs_r = rho_r / Z
KL += _single_basis_KL(target_probs_r, nn_probs_r)
KL /= float(len(bases))
return KL.item()