Skip to content

QNN Config

This module implements the QNN config class.

AnsatzConfig(depth=1, ansatz_type=AnsatzType.HEA, ansatz_strategy=Strategy.DIGITAL, strategy_args=dict(), m_block_qubits=None, param_prefix='theta', tag=None) dataclass

ansatz_strategy = Strategy.DIGITAL class-attribute instance-attribute

Ansatz strategy.

Strategy.DIGITAL for fully digital ansatz. Required if ansatz_type is AnsatzType.ALA. Strategy.SDAQC for analog entangling block. Only available for AnsatzType.HEA or AnsatzType.ALA. Strategy.RYDBERG for fully rydberg hea ansatz. Only available for AnsatzType.HEA.

ansatz_type = AnsatzType.HEA class-attribute instance-attribute

What type of ansatz.

AnsatzType.HEA for Hardware Efficient Ansatz. AnsatzType.IIA for Identity Intialized Ansatz. AnsatzType.ALA for Alternating Layer Ansatz.

depth = 1 class-attribute instance-attribute

Number of layers of the ansatz.

m_block_qubits = None class-attribute instance-attribute

The number of qubits in the local entangling block of an Alternating Layer Ansatz (ALA).

Only used when ansatz_type is AnsatzType.ALA.

param_prefix = 'theta' class-attribute instance-attribute

The base bame of the variational parameter.

strategy_args = field(default_factory=dict) class-attribute instance-attribute

A dictionary containing keyword arguments to the function creating the ansatz.

Details about each below.

For Strategy.DIGITAL strategy, accepts the following: periodic (bool): if the qubits should be linked periodically. periodic=False is not supported in emu-c. operations (list): list of operations to cycle through in the digital single-qubit rotations of each layer. Defaults to [RX, RY, RX] for hea and [RX, RY] for iia. entangler (AbstractBlock): 2-qubit entangling operation. Supports CNOT, CZ, CRX, CRY, CRZ, CPHASE. Controlld rotations will have variational parameters on the rotation angles. Defaults to CNOT

For Strategy.SDAQC strategy, accepts the following: operations (list): list of operations to cycle through in the digital single-qubit rotations of each layer. Defaults to [RX, RY, RX] for hea and [RX, RY] for iia. entangler (AbstractBlock): Hamiltonian generator for the analog entangling layer. Time parameter is considered variational. Defaults to NN interaction.

For Strategy.RYDBERG strategy, accepts the following: addressable_detuning: whether to turn on the trainable semi-local addressing pattern on the detuning (n_i terms in the Hamiltonian). Defaults to True. addressable_drive: whether to turn on the trainable semi-local addressing pattern on the drive (sigma_i^x terms in the Hamiltonian). Defaults to False. tunable_phase: whether to have a tunable phase to get both sigma^x and sigma^y rotations in the drive term. If False, only a sigma^x term will be included in the drive part of the Hamiltonian generator. Defaults to False.

tag = None class-attribute instance-attribute

String to indicate the name tag of the ansatz.

Defaults to None, in which case no tag will be applied.

FeatureMapConfig(num_features=0, basis_set=BasisSet.FOURIER, reupload_scaling=ReuploadScaling.CONSTANT, feature_range=None, target_range=None, multivariate_strategy=MultivariateStrategy.PARALLEL, feature_map_strategy=Strategy.DIGITAL, param_prefix=None, num_repeats=0, operation=None, inputs=None, tag=None) dataclass

basis_set = BasisSet.FOURIER class-attribute instance-attribute

Basis set for feature encoding.

Takes qadence.BasisSet. Give a single BasisSet to use the same for all features. Give a dict of (str, BasisSet) where the key is the name of the variable and the value is the BasisSet to use for encoding that feature. BasisSet.FOURIER for Fourier encoding. BasisSet.CHEBYSHEV for Chebyshev encoding.

feature_map_strategy = Strategy.DIGITAL class-attribute instance-attribute

Strategy for feature map.

Accepts DIGITAL, ANALOG or RYDBERG. Defaults to DIGITAL. If the strategy is incompatible with the operation chosen, then operation gets preference and the given strategy is ignored.

feature_range = None class-attribute instance-attribute

Range of data that the input data is assumed to come from.

Give a single tuple to use the same range for all features. Give a dict of (str, tuple) where the key is the name of the variable and the value is the feature range to use for that feature.

inputs = None class-attribute instance-attribute

List that indicates the order of variables of the tensors that are passed.

Optional if a single feature is being encoded, required otherwise. Given input tensors xs = torch.rand(batch_size, input_size:=2) a QNN with inputs=["t", "x"] will assign t, x = xs[:,0], xs[:,1].

multivariate_strategy = MultivariateStrategy.PARALLEL class-attribute instance-attribute

The encoding strategy in case of multi-variate function.

Takes qadence.MultivariateStrategy. If PARALLEL, the features are encoded in one block of rotation gates with the register being split in sub-registers for each feature. If SERIES, the features are encoded sequentially using the full register for each feature, with an ansatz block between them. PARALLEL is allowed only for DIGITAL feature_map_strategy.

num_features = 0 class-attribute instance-attribute

Number of feature parameters to be encoded.

Defaults to 0. Thus, no feature parameters are encoded.

num_repeats = 0 class-attribute instance-attribute

Number of feature map layers repeated in the data reuploading step.

If all features are to be repeated the same number of times, then can give a single int. For different number of repetitions for each feature, provide a dict of (str, int) where the key is the name of the variable and the value is the number of repetitions for that feature. This amounts to the number of additional reuploads. So if num_repeats is N, the data gets uploaded N+1 times. Defaults to no repetition.

operation = None class-attribute instance-attribute

Type of operation.

Choose among the analog or digital rotations or a custom callable function returning an AnalogBlock instance. If the type of operation is incompatible with the strategy chosen, then operation gets preference and the given strategy is ignored.

param_prefix = None class-attribute instance-attribute

String prefix to create trainable parameters in Feature Map.

A string prefix to create trainable parameters multiplying the feature parameter inside the feature-encoding function. Note that currently this does not take into account the domain of the feature-encoding function. Defaults to None and thus, the feature map is not trainable. Note that this is separate from the name of the parameter. The user can provide a single prefix for all features, and it will be appended by appropriate feature name automatically.

reupload_scaling = ReuploadScaling.CONSTANT class-attribute instance-attribute

Scaling for encoding the same feature on different qubits.

Scaling used to encode the same feature on different qubits in the same layer of the feature maps. Takes qadence.ReuploadScaling. Give a single ReuploadScaling to use the same for all features. Give a dict of (str, ReuploadScaling) where the key is the name of the variable and the value is the ReuploadScaling to use for encoding that feature. ReuploadScaling.CONSTANT for constant scaling. ReuploadScaling.TOWER for linearly increasing scaling. ReuploadScaling.EXP for exponentially increasing scaling.

tag = None class-attribute instance-attribute

String to indicate the name tag of the feature map.

Defaults to None, in which case no tag will be applied.

target_range = None class-attribute instance-attribute

Range of data the data encoder assumes as natural range.

Give a single tuple to use the same range for all features. Give a dict of (str, tuple) where the key is the name of the variable and the value is the target range to use for that feature.