Simplified Quantum Layer (QLayerV2)

QLayerV2 is a new, simplified quantum layer for hybrid QNNs. It is constructed from a synthesized quantum program and an optional set of observables, and produces a trainable torch.nn.Module whose outputs are the expectation values of those observables. The new interface is designed to be more intuitive than the classic QLayer and - just as importantly - it is much faster behind the scenes, so training loops run considerably quicker. Use it when your quantum layer’s output is naturally a set of expectation values. For layers that need sampling, counts, or custom classical post-processing, keep using the classic QLayer (see Choosing between the two layers).

QLayerV2 currently runs only inside Classiq Studio and is limited to circuits of up to 12 qubits. Constructing it in any other environment, or with a larger circuit, raises an error. Extending its scale and reach is planned.

QLayerV2 is a temporary name. It ships alongside the existing QLayer (also available under the alias QLayerV1), which is unchanged for now so existing code keeps working. QLayerV2 is the interface we are moving toward: once it covers the full feature set, the classic interface will go through the standard deprecation process and QLayerV2 will become the single, canonical QLayer. That deprecation has not started yet.

Interface

QLayerV2 is imported from the same package as QLayer:

from classiq.applications.qnn import QLayerV2

QLayerV2(
    qprog,             # a synthesized QuantumProgram
    observables=None,  # optional; defaults to one <Z_i> per qubit
)

qprog - a synthesized QuantumProgram. Its parameters are classified by name: the input/i_ prefixes mark inputs (the encoded classical data) and the weight/w_ prefixes mark trainable weights (for example input_0, i_x, weight_0, w_theta). See Parameters: Inputs Versus Weights.
observables - one output feature per Pauli observable. Defaults to one <Z_i> per qubit, so the default output width equals the circuit width. Accepts a single SparsePauliOp or a sequence of them. An observable narrower than the program is padded with identities to the program’s width.

The layer trains like any other torch module. Its forward is rank-preserving: an input of shape (batch, in_features) produces (batch, out_features), and an unbatched input of shape (in_features,) produces (out_features,).

Examples

Minimal example

With only a qprog, the layer measures one <Z_i> per qubit using the default observables:

from classiq import *
from classiq.applications.qnn import QLayerV2


@qfunc
def main(input_0: CReal, weight_0: CReal, res: Output[QArray]) -> None:
    allocate(2, res)
    RY(input_0, res[0])   # driven by the input feature
    RY(weight_0, res[1])  # trainable weight
    CX(res[0], res[1])


qprog = synthesize(main)

# Defaults: one <Z_i> per qubit.
layer = QLayerV2(qprog)

The result is a trainable torch layer that plugs into an ordinary network:

import torch.nn as nn


class MyNet(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.pre = nn.Linear(4, 1)
        self.quantum = QLayerV2(qprog)

    def forward(self, x):
        x = self.pre(x)
        return self.quantum(x)

Custom observables

Pass one SparsePauliOp per output feature to control what the layer measures. Here the single output feature is <Z_0>:

from classiq.qmod.builtins.enums import Pauli

# <Z_0>: Pauli-Z on qubit 0. Narrower observables are padded to the circuit width.
z0 = Pauli.Z(0)

layer = QLayerV2(qprog, observables=[z0])

Choosing between the two layers

The classic QLayer is also available under the alias QLayerV1, so the two interfaces can be named side by side during the transition.

	`QLayerV1` (alias of the classic `QLayer`)	`QLayerV2` (new)
Output	Any tensor from a custom `post_process`	Expectation values of Pauli observables
You provide	`execute` and `post_process` callables	Just `qprog` (and optional `observables`)
Measurement	Sampling derived values	Expectation values only
Speed	Standard	Much faster
Runs	Anywhere execution is available	Classiq Studio only, up to 12 qubits

QLayerV1 remains available today for sampling, counts, and custom post-processing. QLayerV2 is the interface we are building toward and will eventually become the single, canonical QLayer. Reach for QLayerV2 when you want the simplest, fastest path to an expectation-value layer.

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Simplified Quantum Layer (QLayerV2)

Interface

Examples

Minimal example

Custom observables

Choosing between the two layers

​Interface

​Examples

​Minimal example

​Custom observables

​Choosing between the two layers

Interface

Examples

Minimal example

Custom observables

Choosing between the two layers