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eigenkit

Layers

Pre-built Layers

The pre-built layers wrap Circuit with reasonable defaults. They are all subclasses of torch.nn.Module, and they can be used directly inside nn.Sequential.

`Basic`: one encoding and one variational block

import torch
from eigenkit import Basic

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

`Reupload`: standard data reuploading

import torch
from eigenkit import Reupload

layer = Reupload(n_qubits=4, depth=3, trainable_scale=True)
out = layer(torch.randn(8, 4))
print(out.shape)  # torch.Size([8, 4])

Here, in_features == n_qubits. Set trainable_scale=True for the trainable feature scaling of Schuld et al. (2021).

`Ensemble`: `N` independent circuits over chunks

import torch
from eigenkit import Ensemble

layer = Ensemble(in_features=20, n_qubits=4, depth=5)
out = layer(torch.randn(128, 20))
print(out.shape)  # torch.Size([128, 20]): 5 circuits * 4 qubits

Inspecting the underlying circuit

The Basic, Reupload and Ensemble layers expose .circuit (or .circuits for Ensemble), so that you can inspect the circuit or save its weights:

layer = Basic(n_qubits=4, depth=2)
print(layer.circuit.spec.n_weights)  # 8 = depth * n_qubits
print(list(layer.parameters()))  # one Parameter of shape (8,), float64,
# randomly initialised unless you seed torch

To draw the circuit, call layer.circuit.draw(), which returns a drawing as text. For a drawing from a backend, build a circuit for a single call, without a batch, and use the drawing method of that backend (for example qml.draw_mpl for PennyLane).