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=True)[source]¶
Initializes a NeuralState from the parameters in the given location.
- Parameters
- Returns
A new NeuralState initialized from the given parameters. The returned NeuralState will be of whichever type this function was called on. An error may be thrown if the loaded parameters correspond to a different type of NeuralState than the caller.
- compute_batch_gradients(k, samples_batch, neg_batch, *args, **kwargs)[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.
*args – Ignored.
**kwargs – Ignored.
- Returns
A single-element list containing the gradients calculated with a Gibbs sampled negative phase update
- Return type
- compute_exact_gradients(samples_batch, space, bases_batch=None)[source]¶
Computes the gradients of the parameters, using exact sampling for the negative phase update instead of Gibbs sampling
- Parameters
samples_batch (torch.Tensor) – The measurements
space (torch.Tensor) – A rank 2 tensor of the entire visible space.
bases_batch (numpy.ndarray) – The bases in which the measurements are made
- Returns
A two-element list containing the amplitude and phase RBM gradients calculated with an exact negative phase update
- Return type
- compute_exact_grads(samples_batch, space, *args, **kwargs)[source]¶
Computes the gradients of the parameters, using exact sampling for the negative phase update instead of Gibbs sampling
- Parameters
samples_batch (torch.Tensor) – The measurements
space (torch.Tensor) – A rank 2 tensor of the entire visible space.
*args – Ignored.
**kwargs – Ignored.
- Returns
A single-element list containing the gradients calculated with an exact negative phase update
- Return type
- compute_normalization(space)[source]¶
Alias for
normalization
- 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, optimizer_args=None, scheduler=None, scheduler_args=None, **kwargs)[source]¶
Train the NeuralState.
- Parameters
data (numpy.ndarray) – 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 (numpy.ndarray) – The measurement bases for each sample. Must be provided if training a ComplexWaveFunction or DensityMatrix.
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.
scheduler – The constructor of a torch scheduler
optimizer_args (dict) – Arguments to pass to the optimizer
scheduler_args (dict) – Arguments to pass to the scheduler
**kwargs – Ignored; exists for backwards compatibility.
- 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, *args, **kwargs)[source]¶
Compute the gradient of the effective energy for a batch of states.
- Parameters
v (torch.Tensor) – visible states
*args – Ignored.
**kwargs – Ignored.
- Returns
A two-element list containing the gradients of the effective energy. The second element will always be zero.
- Return type
- importance_sampling_denominator(v)[source]¶
Compute the denominator of the weight of an arbitrary sample, with respect to the sample v.
In the case of a mixed state, this quantity is , while in the pure case it is
- Parameters
v (torch.Tensor) – A batch containing the samples
- Returns
A complex tensor containing the denominator of the weights with respect to
- Return type
- importance_sampling_numerator(vp, v)[source]¶
Compute the numerator of the weight of sample vp, with respect to the sample v.
In the case of a mixed state, this quantity is , while in the pure case it is
- Parameters
vp (torch.Tensor) – A batch containing the samples
v (torch.Tensor) – A batch containing the samples
- Returns
A complex tensor containing the numerator of the weights of with respect to
- Return type
- importance_sampling_weight(vp, v)[source]¶
Compute the weight of sample vp, with respect to the sample v.
In the case of a mixed state, this ratio is:
While in the pure case:
- Parameters
vp (torch.Tensor) – A batch containing the samples
v (torch.Tensor) – A batch containing the samples
- Returns
A complex tensor containing the weights of with respect to
- Return type
- load(location)[source]¶
Loads the NeuralState parameters from the given location ignoring any metadata stored in the file. Overwrites the NeuralState’s parameters.
Note
The NeuralState 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 NeuralState 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.
- normalization(space)[source]¶
Compute the normalization constant of the state. In the case of a pure state, this is the norm of the unnormalized wavefunction. In the case of a mixed state, this is the trace of the unnormalized density matrix.
- Parameters
space (torch.Tensor) – A rank 2 tensor of the entire visible space.
- 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
- positive_phase_gradients(samples_batch, *args, **kwargs)[source]¶
Computes the positive phase of the gradients of the parameters.
- Parameters
samples_batch (torch.Tensor) – The measurements
*args – Ignored.
**kwargs – Ignored.
- Returns
A two-element list containing the gradients of the effective energy. The second element will always be zero.
- Return type
- probability(v, Z=1.0)[source]¶
Evaluates the probability of the given vector(s) of visible states. Assumes the visible states were measured in the computational basis.
- Parameters
v (torch.Tensor) – The visible states.
Z (float) – The partition function / normalization constant. Defaults to 1, producing unnormalized probabilities.
- 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 NeuralState 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=False, module=None)[source]¶
Bases:
qucumber.nn_states.WaveFunctionBase
Class capable of learning wavefunctions with a non-zero 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.
- am_grads(v)[source]¶
Computes the gradients of the amplitude RBM for given input states
- Parameters
v (torch.Tensor) – The input state,
- Returns
The gradients of all amplitude RBM parameters
- Return type
- 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 NeuralState from the parameters in the given location.
- Parameters
- Returns
A new NeuralState initialized from the given parameters. The returned NeuralState will be of whichever type this function was called on. An error may be thrown if the loaded parameters correspond to a different type of NeuralState than the caller.
- 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 (numpy.ndarray) – Batch of the input bases corresponding to the samples in samples_batch.
- Returns
A two-element list containing the amplitude and phase RBM gradients calculated with a Gibbs sampled negative phase update
- Return type
- compute_exact_gradients(samples_batch, space, bases_batch=None)[source]¶
Computes the gradients of the parameters, using exact sampling for the negative phase update instead of Gibbs sampling
- Parameters
samples_batch (torch.Tensor) – The measurements
space (torch.Tensor) – A rank 2 tensor of the entire visible space.
bases_batch (numpy.ndarray) – The bases in which the measurements are made
- Returns
A two-element list containing the amplitude and phase RBM gradients calculated with an exact negative phase update
- Return type
- compute_normalization(space)[source]¶
Alias for
normalization
- 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, optimizer_args=None, scheduler=None, scheduler_args=None, **kwargs)[source]¶
Train the NeuralState.
- Parameters
data (numpy.ndarray) – 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 (numpy.ndarray) – The measurement bases for each sample. Must be provided if training a ComplexWaveFunction or DensityMatrix.
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.
scheduler – The constructor of a torch scheduler
optimizer_args (dict) – Arguments to pass to the optimizer
scheduler_args (dict) – Arguments to pass to the scheduler
**kwargs – Ignored; exists for backwards compatibility.
- 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(samples, bases=None)[source]¶
Compute the gradient of a batch of sample, measured in given bases.
- Parameters
sample (numpy.ndarray) – A batch of samples to compute the gradient of.
basis (numpy.ndarray or list[str] or None) – A batch of bases.
- Returns
A list of 2 tensors containing the accumulated gradients of each of the internal RBMs.
- Return type
- importance_sampling_denominator(v)[source]¶
Compute the denominator of the weight of an arbitrary sample, with respect to the sample v.
In the case of a mixed state, this quantity is , while in the pure case it is
- Parameters
v (torch.Tensor) – A batch containing the samples
- Returns
A complex tensor containing the denominator of the weights with respect to
- Return type
- importance_sampling_numerator(vp, v)[source]¶
Compute the numerator of the weight of sample vp, with respect to the sample v.
In the case of a mixed state, this quantity is , while in the pure case it is
- Parameters
vp (torch.Tensor) – A batch containing the samples
v (torch.Tensor) – A batch containing the samples
- Returns
A complex tensor containing the numerator of the weights of with respect to
- Return type
- importance_sampling_weight(vp, v)[source]¶
Compute the weight of sample vp, with respect to the sample v.
In the case of a mixed state, this ratio is:
While in the pure case:
- Parameters
vp (torch.Tensor) – A batch containing the samples
v (torch.Tensor) – A batch containing the samples
- Returns
A complex tensor containing the weights of with respect to
- Return type
- load(location)[source]¶
Loads the NeuralState parameters from the given location ignoring any metadata stored in the file. Overwrites the NeuralState’s parameters.
Note
The NeuralState 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 NeuralState 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.
- normalization(space)[source]¶
Compute the normalization constant of the state. In the case of a pure state, this is the norm of the unnormalized wavefunction. In the case of a mixed state, this is the trace of the unnormalized density matrix.
- Parameters
space (torch.Tensor) – A rank 2 tensor of the entire visible space.
- ph_grads(v)[source]¶
Computes the gradients of the phase RBM for given input states
- Parameters
v (torch.Tensor) – The input state,
- Returns
The gradients of all phase RBM parameters
- Return type
- 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
- positive_phase_gradients(samples_batch, bases_batch=None)[source]¶
Computes the positive phase of the gradients of the parameters.
- Parameters
samples_batch (torch.Tensor) – The measurements
bases_batch (numpy.ndarray) – The bases in which the measurements are made
- Returns
A two-element list containing the amplitude and phase RBM gradients
- Return type
- probability(v, Z=1.0)[source]¶
Evaluates the probability of the given vector(s) of visible states. Assumes the visible states were measured in the computational basis.
- Parameters
v (torch.Tensor) – The visible states.
Z (float) – The partition function / normalization constant. Defaults to 1, producing unnormalized probabilities.
- 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.
- rotated_gradient(basis, sample)[source]¶
Computes the gradients rotated into the measurement basis
- Parameters
basis (numpy.ndarray) – The bases in which the measurement is made
sample (torch.Tensor) – The measurement (either 0 or 1)
- Returns
A list of two tensors, representing the rotated gradients of the amplitude and phase RBMS
- Return type
- 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 NeuralState 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
Density Matrix¶
- class qucumber.nn_states.DensityMatrix(num_visible, num_hidden=None, num_aux=None, unitary_dict=None, gpu=False, module=None)[source]¶
Bases:
qucumber.nn_states.NeuralStateBase
- Parameters
num_visible (int) – The number of visible units, i.e. the size of the system
num_hidden (int) – The number of units in the hidden layer
num_aux (int) – The number of units in the purification layer
unitary_dict (dict[str, torch.Tensor]) – A dictionary associating bases with their unitary rotations
gpu (bool) – Whether to perform computations on the default gpu.
- am_grads(v)[source]¶
Computes the gradients of the amplitude RBM for given input states
- Parameters
v (torch.Tensor) – The first input state,
- Returns
The gradients of all amplitude RBM parameters
- Return type
- static autoload(location, gpu=False)[source]¶
Initializes a NeuralState from the parameters in the given location.
- Parameters
- Returns
A new NeuralState initialized from the given parameters. The returned NeuralState will be of whichever type this function was called on. An error may be thrown if the loaded parameters correspond to a different type of NeuralState than the caller.
- 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 (numpy.ndarray) – Batch of the input bases corresponding to the samples in samples_batch.
- Returns
A two-element list containing the amplitude and phase RBM gradients calculated with a Gibbs sampled negative phase update
- Return type
- compute_exact_gradients(samples_batch, space, bases_batch=None)[source]¶
Computes the gradients of the parameters, using exact sampling for the negative phase update instead of Gibbs sampling
- Parameters
samples_batch (torch.Tensor) – The measurements
space (torch.Tensor) – A rank 2 tensor of the entire visible space.
bases_batch (numpy.ndarray) – The bases in which the measurements are made
- Returns
A two-element list containing the amplitude and phase RBM gradients calculated with an exact negative phase update
- Return type
- compute_normalization(space)[source]¶
Alias for
normalization
- property device¶
The device that the model is on.
- fit(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)[source]¶
Train the NeuralState.
- Parameters
data (numpy.ndarray) – 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 (numpy.ndarray) – The measurement bases for each sample. Must be provided if training a ComplexWaveFunction or DensityMatrix.
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.
scheduler – The constructor of a torch scheduler
optimizer_args (dict) – Arguments to pass to the optimizer
scheduler_args (dict) – Arguments to pass to the scheduler
**kwargs – Ignored; exists for backwards compatibility.
- 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(samples, bases=None)[source]¶
Compute the gradient of a batch of sample, measured in given bases.
- Parameters
sample (numpy.ndarray) – A batch of samples to compute the gradient of.
basis (numpy.ndarray or list[str] or None) – A batch of bases.
- Returns
A list of 2 tensors containing the accumulated gradients of each of the internal RBMs.
- Return type
- importance_sampling_denominator(v)[source]¶
Compute the denominator of the weight of an arbitrary sample, with respect to the sample v.
In the case of a mixed state, this quantity is , while in the pure case it is
- Parameters
v (torch.Tensor) – A batch containing the samples
- Returns
A complex tensor containing the denominator of the weights with respect to
- Return type
- importance_sampling_numerator(vp, v)[source]¶
Compute the numerator of the weight of sample vp, with respect to the sample v.
In the case of a mixed state, this quantity is , while in the pure case it is
- Parameters
vp (torch.Tensor) – A batch containing the samples
v (torch.Tensor) – A batch containing the samples
- Returns
A complex tensor containing the numerator of the weights of with respect to
- Return type
- importance_sampling_weight(vp, v)[source]¶
Compute the weight of sample vp, with respect to the sample v.
In the case of a mixed state, this ratio is:
While in the pure case:
- Parameters
vp (torch.Tensor) – A batch containing the samples
v (torch.Tensor) – A batch containing the samples
- Returns
A complex tensor containing the weights of with respect to
- Return type
- load(location)[source]¶
Loads the NeuralState parameters from the given location ignoring any metadata stored in the file. Overwrites the NeuralState’s parameters.
Note
The NeuralState 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 NeuralState 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.
- normalization(space)[source]¶
Compute the normalization constant of the state. In the case of a pure state, this is the norm of the unnormalized wavefunction. In the case of a mixed state, this is the trace of the unnormalized density matrix.
- Parameters
space (torch.Tensor) – A rank 2 tensor of the entire visible space.
- ph_grads(v)[source]¶
Computes the gradients of the phase RBM for given input states
- Parameters
v (torch.Tensor) – The first input state,
- Returns
The gradients of all phase RBM parameters
- Return type
- pi(v, vp, expand=True)[source]¶
Calculates elements of the matrix. If expand is True, will return a complex matrix . Otherwise will return a complex vector .
- Parameters
v (torch.Tensor) – A batch of visible states, .
vp (torch.Tensor) – The other batch of visible state, .
expand (bool) – Whether to return a matrix (True) or a vector (False).
- Returns
The matrix elements given by
- Return type
- pi_grad(v, vp, phase=False, expand=False)[source]¶
- Calculates the gradient of the matrix with
respect to the amplitude RBM parameters for two input states
- Parameters
v (torch.Tensor) – One of the visible states,
vp (torch.Tensor) – The other visible state, :math`sigma’`
phase (bool) – Whether to compute the gradients for the phase RBM (True) or the amplitude RBM (False)
- Returns
The matrix element of the gradient given by
- Return type
- positive_phase_gradients(samples_batch, bases_batch=None)[source]¶
Computes the positive phase of the gradients of the parameters.
- Parameters
samples_batch (torch.Tensor) – The measurements
bases_batch (numpy.ndarray) – The bases in which the measurements are made
- Returns
A two-element list containing the amplitude and phase RBM gradients
- Return type
- probability(v, Z=1.0)[source]¶
Evaluates the probability of the given vector(s) of visible states. Assumes the visible states were measured in the computational basis.
- Parameters
v (torch.Tensor) – The visible states.
Z (float) – The partition function / normalization constant. Defaults to 1, producing unnormalized probabilities.
- Returns
The probability of the given vector(s) of visible units.
- 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.
- rho(v, vp=None, expand=True)[source]¶
Computes the matrix elements of the (unnormalized) density matrix. If expand is True, will return a complex matrix . Otherwise will return a complex vector .
- Parameters
v (torch.Tensor) – One of the visible states, .
vp (torch.Tensor) – The other visible state, . If None, will be set to v.
expand (bool) – Whether to return a matrix (True) or a vector (False).
- Returns
The elements of the current density matrix
- Return type
- rotated_gradient(basis, sample)[source]¶
Computes the gradients rotated into the measurement basis
- Parameters
basis (numpy.ndarray) – The bases in which the measurement is made
sample (torch.Tensor) – The measurement (either 0 or 1)
- Returns
A list of two tensors, representing the rotated gradients of the amplitude and phase RBMs
- Return type
- 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 NeuralState 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:
qucumber.nn_states.NeuralStateBase
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
- importance_sampling_denominator(v)[source]¶
Compute the denominator of the weight of an arbitrary sample, with respect to the sample v.
In the case of a mixed state, this quantity is , while in the pure case it is
- Parameters
v (torch.Tensor) – A batch containing the samples
- Returns
A complex tensor containing the denominator of the weights with respect to
- Return type
- importance_sampling_numerator(vp, v)[source]¶
Compute the numerator of the weight of sample vp, with respect to the sample v.
In the case of a mixed state, this quantity is , while in the pure case it is
- Parameters
vp (torch.Tensor) – A batch containing the samples
v (torch.Tensor) – A batch containing the samples
- Returns
A complex tensor containing the numerator of the weights of with respect to
- Return type
- 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
- 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 NeuralState¶
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.NeuralStateBase[source]¶
Bases:
abc.ABC
Abstract Base Class for Neural Network Quantum States.
- abstract static autoload(location, gpu=False)[source]¶
Initializes a NeuralState from the parameters in the given location.
- Parameters
- Returns
A new NeuralState initialized from the given parameters. The returned NeuralState will be of whichever type this function was called on. An error may be thrown if the loaded parameters correspond to a different type of NeuralState than the caller.
- 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 (numpy.ndarray) – Batch of the input bases corresponding to the samples in samples_batch.
- Returns
A two-element list containing the amplitude and phase RBM gradients calculated with a Gibbs sampled negative phase update
- Return type
- compute_exact_gradients(samples_batch, space, bases_batch=None)[source]¶
Computes the gradients of the parameters, using exact sampling for the negative phase update instead of Gibbs sampling
- Parameters
samples_batch (torch.Tensor) – The measurements
space (torch.Tensor) – A rank 2 tensor of the entire visible space.
bases_batch (numpy.ndarray) – The bases in which the measurements are made
- Returns
A two-element list containing the amplitude and phase RBM gradients calculated with an exact negative phase update
- Return type
- compute_normalization(space)[source]¶
Alias for
normalization
- 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, optimizer_args=None, scheduler=None, scheduler_args=None, **kwargs)[source]¶
Train the NeuralState.
- Parameters
data (numpy.ndarray) – 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 (numpy.ndarray) – The measurement bases for each sample. Must be provided if training a ComplexWaveFunction or DensityMatrix.
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.
scheduler – The constructor of a torch scheduler
optimizer_args (dict) – Arguments to pass to the optimizer
scheduler_args (dict) – Arguments to pass to the scheduler
**kwargs – Ignored; exists for backwards compatibility.
- 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(samples, bases=None)[source]¶
Compute the gradient of a batch of sample, measured in given bases.
- Parameters
sample (numpy.ndarray) – A batch of samples to compute the gradient of.
basis (numpy.ndarray or list[str] or None) – A batch of bases.
- Returns
A list of 2 tensors containing the accumulated gradients of each of the internal RBMs.
- Return type
- abstract importance_sampling_denominator(v)[source]¶
Compute the denominator of the weight of an arbitrary sample, with respect to the sample v.
In the case of a mixed state, this quantity is , while in the pure case it is
- Parameters
v (torch.Tensor) – A batch containing the samples
- Returns
A complex tensor containing the denominator of the weights with respect to
- Return type
- abstract importance_sampling_numerator(vp, v)[source]¶
Compute the numerator of the weight of sample vp, with respect to the sample v.
In the case of a mixed state, this quantity is , while in the pure case it is
- Parameters
vp (torch.Tensor) – A batch containing the samples
v (torch.Tensor) – A batch containing the samples
- Returns
A complex tensor containing the numerator of the weights of with respect to
- Return type
- importance_sampling_weight(vp, v)[source]¶
Compute the weight of sample vp, with respect to the sample v.
In the case of a mixed state, this ratio is:
While in the pure case:
- Parameters
vp (torch.Tensor) – A batch containing the samples
v (torch.Tensor) – A batch containing the samples
- Returns
A complex tensor containing the weights of with respect to
- Return type
- load(location)[source]¶
Loads the NeuralState parameters from the given location ignoring any metadata stored in the file. Overwrites the NeuralState’s parameters.
Note
The NeuralState 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 NeuralState 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.
- normalization(space)[source]¶
Compute the normalization constant of the state. In the case of a pure state, this is the norm of the unnormalized wavefunction. In the case of a mixed state, this is the trace of the unnormalized density matrix.
- Parameters
space (torch.Tensor) – A rank 2 tensor of the entire visible space.
- positive_phase_gradients(samples_batch, bases_batch=None)[source]¶
Computes the positive phase of the gradients of the parameters.
- Parameters
samples_batch (torch.Tensor) – The measurements
bases_batch (numpy.ndarray) – The bases in which the measurements are made
- Returns
A two-element list containing the amplitude and phase RBM gradients
- Return type
- probability(v, Z=1.0)[source]¶
Evaluates the probability of the given vector(s) of visible states. Assumes the visible states were measured in the computational basis.
- Parameters
v (torch.Tensor) – The visible states.
Z (float) – The partition function / normalization constant. Defaults to 1, producing unnormalized probabilities.
- Returns
The probability of the given vector(s) of visible units.
- 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 NeuralState 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