Copy page

• View as Markdown
• Open in Claude
• Open in ChatGPT
• Connect via MCP

ensemble

eigenkit.torch.layers.ensemble

An ensemble of independent quantum circuits, one for each chunk of the input.

It splits the input into chunks of n_qubits features and runs one independent circuit on each chunk, each with its own weights. The outputs are concatenated along the feature dimension.

Ensemble

Bases: Module

An ensemble of independent quantum circuits, one for each chunk of the input.

Parameters:

Name	Type	Description	Default
in_features	int	The total number of input features. It must be a multiple of n_qubits.	required
n_qubits	int	The number of qubits of each circuit.	required
depth	int	The number of variational layers of each circuit.	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". Default: "RY".	'RY'
variational_type	Literal['basic', 'strong']	"basic" or "strong". Default: "strong".	'strong'
basis	Literal['X', 'Y', 'Z']	The Pauli measurement basis: "X", "Y" or "Z". Default: "Z".	'Z'
provider	ProviderSpec	The backend that runs the circuits. Default: "numpy".	'numpy'

Example

import torch
from eigenkit import Ensemble

layer = Ensemble(in_features=20, n_qubits=4, depth=5)
out = layer(torch.randn(128, 20))  # shape (128, 20)

provider property

The backend that the circuits of this layer were 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 circuits run 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.