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basic

eigenkit.torch.layers.basic

The basic quantum layer: one Encoding, one Variational block and a measurement.

It maps n_qubits input features to n_qubits expectation values, one for each qubit, in the chosen Pauli basis. For data reuploading or ensembles, see Reupload and Ensemble.

Basic

Bases: Module

One Encoding, one Variational block and a measurement on each qubit.

Parameters:

Name	Type	Description	Default
n_qubits	int	The number of qubits, which is also the number of input features.	required
depth	int	The number of variational layers.	required
enc_rotation	Literal['RX', 'RY', 'RZ']	The encoding rotation: "RX", "RY" or "RZ". Default: "RX".	'RX'
var_rotation	Literal['RX', 'RY', 'RZ']	The variational rotation. It is used only when variational_type="basic". The default, "RY", is off both the "RX" encoding axis and the "Z" measurement axis, and this is what lets the weights change the output. Default: "RY".	'RY'
variational_type	Literal['basic', 'strong']	"basic", for one rotation and a controlled-NOT (CNOT) ladder in each layer, or "strong", for RZ, RY and RZ rotations and a CNOT ladder in each layer. Default: "basic".	'basic'
basis	Literal['X', 'Y', 'Z']	The Pauli measurement basis: "X", "Y" or "Z". Default: "Z".	'Z'
provider	ProviderSpec	The backend that runs the circuit. Default: "numpy".	'numpy'

Example

import torch
from eigenkit import Basic

layer = Basic(n_qubits=4, depth=2)
out = layer(torch.randn(8, 4))  # shape (8, 4)

provider property

The backend that the circuit of this layer was compiled for.

The constructor takes provider= in the same forms as Circuit (a registered name, a configuration or a backend object) and turns it into one Provider. This property returns it. The registered name is provider.name.

Returns:

Name	Type	Description
provider	Provider	The backend that the circuit runs on.

forward(x)

Run the layer.

Parameters:

Name	Type	Description	Default
x	Tensor	The input batch, of shape (batch, in_features).	required

Returns:

Name	Type	Description
out	Tensor	The output of the layer for that batch.