Quantum States¶
Positive WaveFunction¶

class
qucumber.nn_states.
PositiveWaveFunction
(num_visible, num_hidden=None, gpu=True, module=None)[source]¶ Bases:
qucumber.nn_states.WaveFunctionBase
Class capable of learning wavefunctions with no phase.
 Parameters
num_visible (int) – The number of visible units, ie. the size of the system being learned.
num_hidden (int) – The number of hidden units in the internal RBM. Defaults to the number of visible units.
gpu (bool) – Whether to perform computations on the default GPU.
module (qucumber.rbm.BinaryRBM) – An instance of a BinaryRBM module to use for density estimation. Will be copied to the default GPU if gpu=True (if it isn’t already there). If None, will initialize a BinaryRBM from scratch.

amplitude
(v)[source]¶ Compute the (unnormalized) amplitude of a given vector/matrix of visible states.
 Parameters
v (torch.Tensor) – visible states
 Returns
Matrix/vector containing the amplitudes of v
 Return type

static
autoload
(location, gpu=False)[source]¶ Initializes a WaveFunction from the parameters in the given location.

compute_batch_gradients
(k, samples_batch, neg_batch)[source]¶ Compute the gradients of a batch of the training data (samples_batch).
 Parameters
k (int) – Number of contrastive divergence steps in training.
samples_batch (torch.Tensor) – Batch of the input samples.
neg_batch (torch.Tensor) – Batch of the input samples for computing the negative phase.
 Returns
List containing the gradients of the parameters.
 Return type

compute_normalization
(space)[source]¶ Compute the normalization constant of the wavefunction.
 Parameters
space (torch.Tensor) – A rank 2 tensor of the entire visible space.

property
device
¶ The device that the model is on.

fit
(data, epochs=100, pos_batch_size=100, neg_batch_size=None, k=1, lr=0.001, progbar=False, starting_epoch=1, time=False, callbacks=None, optimizer=torch.optim.SGD, **kwargs)[source]¶ Train the WaveFunction.
 Parameters
data (np.array) – The training samples
epochs (int) – The number of full training passes through the dataset. Technically, this specifies the index of the last training epoch, which is relevant if starting_epoch is being set.
pos_batch_size (int) – The size of batches for the positive phase taken from the data.
neg_batch_size (int) – The size of batches for the negative phase taken from the data. Defaults to pos_batch_size.
k (int) – The number of contrastive divergence steps.
lr (float) – Learning rate
progbar (bool or str) – Whether or not to display a progress bar. If “notebook” is passed, will use a Jupyter notebook compatible progress bar.
starting_epoch (int) – The epoch to start from. Useful if continuing training from a previous state.
callbacks (list[qucumber.callbacks.CallbackBase]) – Callbacks to run while training.
optimizer (torch.optim.Optimizer) – The constructor of a torch optimizer.
kwargs – Keyword arguments to pass to the optimizer

generate_hilbert_space
(size=None, device=None)[source]¶ Generates Hilbert space of dimension .
 Parameters
size (int) – The size of each element of the Hilbert space. Defaults to the number of visible units.
device – The device to create the Hilbert space matrix on. Defaults to the device this model is on.
 Returns
A tensor with all the basis states of the Hilbert space.
 Return type

gradient
(v)[source]¶ Compute the gradient of the effective energy for a batch of states.
 Parameters
v (torch.Tensor) – visible states
 Returns
A single tensor containing all of the parameter gradients.
 Return type

load
(location)[source]¶ Loads the WaveFunction parameters from the given location ignoring any metadata stored in the file. Overwrites the WaveFunction’s parameters.
Note
The WaveFunction object on which this function is called must have the same parameter shapes as the one who’s parameters are being loaded.
 Parameters
location (str or file) – The location to load the WaveFunction parameters from.

property
max_size
¶ Maximum size of the Hilbert space for full enumeration

property
networks
¶ A list of the names of the internal RBMs.

phase
(v)[source]¶ Compute the phase of a given vector/matrix of visible states.
In the case of a PositiveWaveFunction, the phase is just zero.
 Parameters
v (torch.Tensor) – visible states
 Returns
Matrix/vector containing the phases of v
 Return type

probability
(v, Z)[source]¶ Evaluates the probability of the given vector(s) of visible states.
 Parameters
v (torch.Tensor) – The visible states.
Z (float) – The partition function.
 Returns
The probability of the given vector(s) of visible units.
 Return type

psi
(v)[source]¶ Compute the (unnormalized) wavefunction of a given vector/matrix of visible states.
 Parameters
v (torch.Tensor) – visible states
 Returns
Complex object containing the value of the wavefunction for each visible state
 Return type

property
rbm_am
¶ The RBM to be used to learn the wavefunction amplitude.

sample
(k, num_samples=1, initial_state=None, overwrite=False)[source]¶ Performs k steps of Block Gibbs sampling. One step consists of sampling the hidden state from the conditional distribution , and sampling the visible state from the conditional distribution .
 Parameters
k (int) – Number of Block Gibbs steps.
num_samples (int) – The number of samples to generate.
initial_state (torch.Tensor) – The initial state of the Markov Chains. If given, num_samples will be ignored.
overwrite (bool) – Whether to overwrite the initial_state tensor, if it is provided.

save
(location, metadata=None)[source]¶ Saves the WaveFunction parameters to the given location along with any given metadata.

property
stop_training
¶ If True, will not train.
If this property is set to True during the training cycle, training will terminate once the current batch or epoch ends (depending on when stop_training was set).

subspace_vector
(num, size=None, device=None)[source]¶ Generates a single vector from the Hilbert space of dimension .
 Parameters
size (int) – The size of each element of the Hilbert space.
num (int) – The specific vector to return from the Hilbert space. Since the Hilbert space can be represented by the set of binary strings of length size, num is equivalent to the decimal representation of the returned vector.
device – The device to create the vector on. Defaults to the device this model is on.
 Returns
A state from the Hilbert space.
 Return type
Complex WaveFunction¶

class
qucumber.nn_states.
ComplexWaveFunction
(num_visible, num_hidden=None, unitary_dict=None, gpu=True, module=None)[source]¶ Bases:
qucumber.nn_states.WaveFunctionBase
Class capable of learning wavefunctions with a nonzero phase.
 Parameters
num_visible (int) – The number of visible units, ie. the size of the system being learned.
num_hidden (int) – The number of hidden units in both internal RBMs. Defaults to the number of visible units.
unitary_dict (dict[str, torch.Tensor]) – A dictionary mapping unitary names to their matrix representations.
gpu (bool) – Whether to perform computations on the default GPU.
module (qucumber.rbm.BinaryRBM) – An instance of a BinaryRBM module to use for density estimation; The given RBM object will be used to estimate the amplitude of the wavefunction, while a copy will be used to estimate the phase of the wavefunction. Will be copied to the default GPU if gpu=True (if it isn’t already there). If None, will initialize the BinaryRBMs from scratch.

amplitude
(v)[source]¶ Compute the (unnormalized) amplitude of a given vector/matrix of visible states.
 Parameters
v (torch.Tensor) – visible states .
 Returns
Vector containing the amplitudes of the given states.
 Return type

static
autoload
(location, gpu=False)[source]¶ Initializes a WaveFunction from the parameters in the given location.

compute_batch_gradients
(k, samples_batch, neg_batch, bases_batch=None)[source]¶ Compute the gradients of a batch of the training data (samples_batch).
If measurements are taken in bases other than the reference basis, a list of bases (bases_batch) must also be provided.
 Parameters
k (int) – Number of contrastive divergence steps in training.
samples_batch (torch.Tensor) – Batch of the input samples.
neg_batch (torch.Tensor) – Batch of the input samples for computing the negative phase.
bases_batch (np.array) – Batch of the input bases corresponding to the samples in samples_batch.
 Returns
List containing the gradients of the parameters.
 Return type

compute_normalization
(space)[source]¶ Compute the normalization constant of the wavefunction.
 Parameters
space (torch.Tensor) – A rank 2 tensor of the entire visible space.

property
device
¶ The device that the model is on.

fit
(data, epochs=100, pos_batch_size=100, neg_batch_size=None, k=1, lr=0.001, input_bases=None, progbar=False, starting_epoch=1, time=False, callbacks=None, optimizer=torch.optim.SGD, **kwargs)[source]¶ Train the WaveFunction.
 Parameters
data (np.array) – The training samples
epochs (int) – The number of full training passes through the dataset. Technically, this specifies the index of the last training epoch, which is relevant if starting_epoch is being set.
pos_batch_size (int) – The size of batches for the positive phase taken from the data.
neg_batch_size (int) – The size of batches for the negative phase taken from the data. Defaults to pos_batch_size.
k (int) – The number of contrastive divergence steps.
lr (float) – Learning rate
input_bases (np.array) – The measurement bases for each sample. Must be provided if training a ComplexWaveFunction.
progbar (bool or str) – Whether or not to display a progress bar. If “notebook” is passed, will use a Jupyter notebook compatible progress bar.
starting_epoch (int) – The epoch to start from. Useful if continuing training from a previous state.
callbacks (list[qucumber.callbacks.CallbackBase]) – Callbacks to run while training.
optimizer (torch.optim.Optimizer) – The constructor of a torch optimizer.
kwargs – Keyword arguments to pass to the optimizer

generate_hilbert_space
(size=None, device=None)[source]¶ Generates Hilbert space of dimension .
 Parameters
size (int) – The size of each element of the Hilbert space. Defaults to the number of visible units.
device – The device to create the Hilbert space matrix on. Defaults to the device this model is on.
 Returns
A tensor with all the basis states of the Hilbert space.
 Return type

gradient
(basis, sample)[source]¶ Compute the gradient of a sample, measured in different bases.
 Parameters
basis (np.array) – A set of bases.
sample (np.array) – A sample to compute the gradient of.
 Returns
A list of 2 tensors containing the parameters of each of the internal RBMs.
 Return type

load
(location)[source]¶ Loads the WaveFunction parameters from the given location ignoring any metadata stored in the file. Overwrites the WaveFunction’s parameters.
Note
The WaveFunction object on which this function is called must have the same parameter shapes as the one who’s parameters are being loaded.
 Parameters
location (str or file) – The location to load the WaveFunction parameters from.

property
max_size
¶ Maximum size of the Hilbert space for full enumeration

property
networks
¶ A list of the names of the internal RBMs.

phase
(v)[source]¶ Compute the phase of a given vector/matrix of visible states.
 Parameters
v (torch.Tensor) – visible states .
 Returns
Vector containing the phases of the given states.
 Return type

probability
(v, Z)[source]¶ Evaluates the probability of the given vector(s) of visible states.
 Parameters
v (torch.Tensor) – The visible states.
Z (float) – The partition function.
 Returns
The probability of the given vector(s) of visible units.
 Return type

psi
(v)[source]¶ Compute the (unnormalized) wavefunction of a given vector/matrix of visible states.
 Parameters
v (torch.Tensor) – visible states
 Returns
Complex object containing the value of the wavefunction for each visible state
 Return type

property
rbm_am
¶ The RBM to be used to learn the wavefunction amplitude.

property
rbm_ph
¶ RBM used to learn the wavefunction phase.

sample
(k, num_samples=1, initial_state=None, overwrite=False)[source]¶ Performs k steps of Block Gibbs sampling. One step consists of sampling the hidden state from the conditional distribution , and sampling the visible state from the conditional distribution .
 Parameters
k (int) – Number of Block Gibbs steps.
num_samples (int) – The number of samples to generate.
initial_state (torch.Tensor) – The initial state of the Markov Chains. If given, num_samples will be ignored.
overwrite (bool) – Whether to overwrite the initial_state tensor, if it is provided.

save
(location, metadata=None)[source]¶ Saves the WaveFunction parameters to the given location along with any given metadata.

property
stop_training
¶ If True, will not train.
If this property is set to True during the training cycle, training will terminate once the current batch or epoch ends (depending on when stop_training was set).

subspace_vector
(num, size=None, device=None)[source]¶ Generates a single vector from the Hilbert space of dimension .
 Parameters
size (int) – The size of each element of the Hilbert space.
num (int) – The specific vector to return from the Hilbert space. Since the Hilbert space can be represented by the set of binary strings of length size, num is equivalent to the decimal representation of the returned vector.
device – The device to create the vector on. Defaults to the device this model is on.
 Returns
A state from the Hilbert space.
 Return type
Abstract WaveFunction¶
Note
This is an Abstract Base Class, it is not meant to be used directly. The following API reference is mostly for developers.

class
qucumber.nn_states.
WaveFunctionBase
[source]¶ Bases:
abc.ABC
Abstract Base Class for WaveFunctions.

amplitude
(v)[source]¶ Compute the (unnormalized) amplitude of a given vector/matrix of visible states.
 Parameters
v (torch.Tensor) – visible states
 Returns
Matrix/vector containing the amplitudes of v
 Return type

abstract static
autoload
(location, gpu=False)[source]¶ Initializes a WaveFunction from the parameters in the given location.

compute_batch_gradients
(k, samples_batch, neg_batch, bases_batch=None)[source]¶ Compute the gradients of a batch of the training data (samples_batch).
If measurements are taken in bases other than the reference basis, a list of bases (bases_batch) must also be provided.
 Parameters
k (int) – Number of contrastive divergence steps in training.
samples_batch (torch.Tensor) – Batch of the input samples.
neg_batch (torch.Tensor) – Batch of the input samples for computing the negative phase.
bases_batch (np.array) – Batch of the input bases corresponding to the samples in samples_batch.
 Returns
List containing the gradients of the parameters.
 Return type

compute_normalization
(space)[source]¶ Compute the normalization constant of the wavefunction.
 Parameters
space (torch.Tensor) – A rank 2 tensor of the entire visible space.

abstract property
device
¶ The device that the model is on.

fit
(data, epochs=100, pos_batch_size=100, neg_batch_size=None, k=1, lr=0.001, input_bases=None, progbar=False, starting_epoch=1, time=False, callbacks=None, optimizer=torch.optim.SGD, **kwargs)[source]¶ Train the WaveFunction.
 Parameters
data (np.array) – The training samples
epochs (int) – The number of full training passes through the dataset. Technically, this specifies the index of the last training epoch, which is relevant if starting_epoch is being set.
pos_batch_size (int) – The size of batches for the positive phase taken from the data.
neg_batch_size (int) – The size of batches for the negative phase taken from the data. Defaults to pos_batch_size.
k (int) – The number of contrastive divergence steps.
lr (float) – Learning rate
input_bases (np.array) – The measurement bases for each sample. Must be provided if training a ComplexWaveFunction.
progbar (bool or str) – Whether or not to display a progress bar. If “notebook” is passed, will use a Jupyter notebook compatible progress bar.
starting_epoch (int) – The epoch to start from. Useful if continuing training from a previous state.
callbacks (list[qucumber.callbacks.CallbackBase]) – Callbacks to run while training.
optimizer (torch.optim.Optimizer) – The constructor of a torch optimizer.
kwargs – Keyword arguments to pass to the optimizer

generate_hilbert_space
(size=None, device=None)[source]¶ Generates Hilbert space of dimension .
 Parameters
size (int) – The size of each element of the Hilbert space. Defaults to the number of visible units.
device – The device to create the Hilbert space matrix on. Defaults to the device this model is on.
 Returns
A tensor with all the basis states of the Hilbert space.
 Return type

load
(location)[source]¶ Loads the WaveFunction parameters from the given location ignoring any metadata stored in the file. Overwrites the WaveFunction’s parameters.
Note
The WaveFunction object on which this function is called must have the same parameter shapes as the one who’s parameters are being loaded.
 Parameters
location (str or file) – The location to load the WaveFunction parameters from.

property
max_size
¶ Maximum size of the Hilbert space for full enumeration

abstract property
networks
¶ A list of the names of the internal RBMs.

abstract
phase
(v)[source]¶ Compute the phase of a given vector/matrix of visible states.
 Parameters
v (torch.Tensor) – visible states
 Returns
Matrix/vector containing the phases of v
 Return type

probability
(v, Z)[source]¶ Evaluates the probability of the given vector(s) of visible states.
 Parameters
v (torch.Tensor) – The visible states.
Z (float) – The partition function.
 Returns
The probability of the given vector(s) of visible units.
 Return type

abstract
psi
(v)[source]¶ Compute the (unnormalized) wavefunction of a given vector/matrix of visible states.
 Parameters
v (torch.Tensor) – visible states
 Returns
Complex object containing the value of the wavefunction for each visible state
 Return type

abstract property
rbm_am
¶ The RBM to be used to learn the wavefunction amplitude.

sample
(k, num_samples=1, initial_state=None, overwrite=False)[source]¶ Performs k steps of Block Gibbs sampling. One step consists of sampling the hidden state from the conditional distribution , and sampling the visible state from the conditional distribution .
 Parameters
k (int) – Number of Block Gibbs steps.
num_samples (int) – The number of samples to generate.
initial_state (torch.Tensor) – The initial state of the Markov Chains. If given, num_samples will be ignored.
overwrite (bool) – Whether to overwrite the initial_state tensor, if it is provided.

save
(location, metadata=None)[source]¶ Saves the WaveFunction parameters to the given location along with any given metadata.

property
stop_training
¶ If True, will not train.
If this property is set to True during the training cycle, training will terminate once the current batch or epoch ends (depending on when stop_training was set).

subspace_vector
(num, size=None, device=None)[source]¶ Generates a single vector from the Hilbert space of dimension .
 Parameters
size (int) – The size of each element of the Hilbert space.
num (int) – The specific vector to return from the Hilbert space. Since the Hilbert space can be represented by the set of binary strings of length size, num is equivalent to the decimal representation of the returned vector.
device – The device to create the vector on. Defaults to the device this model is on.
 Returns
A state from the Hilbert space.
 Return type
