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Quantum Layer

The Classiq engine exports the QLayer object, which inherits from torch.nn.Module (like most objects in the torch.nn namespace), and it acts like one. The QLayer object is defined like this: 
class QLayer(nn.Module):
    def __init__(
        self,
        quantum_program: QuantumProgram,
        execute: ExecuteFunction,
        post_process: PostProcessFunction,
    ) -> None: ...
Or, 
class QLayer(nn.Module):
    def __init__(
        self,
        quantum_program: QuantumProgram,
        post_process: PostProcessFunction,
    ) -> None: ...
The first parameter, quantum_program, is the result of synthesizing a quantum model. Note that the parameters are assumed to follow the API stated in qnn. The second parameter is a callable which is responsible for executing the quantum program, usually with execute_qnn. It takes a QuantumProgram and MultipleArguments (a list of arguments sets to assign to the quantum program parameters) as inputs, and returns a ResultsCollection. Note that this argument can be left out, as demonstrated in the second code block. If it is not supplied, the layer will create an ExecutionSession and sample the quantum program automagically.
In order to properly close the ExecutionSession, if it was created, call the teardown method of QLayer.
The third parameter is a callable which is responsible for post-processing each execution result. It takes a SavedResult as input, process it and returns a Tensor. An example of such callables: 
import torch

from classiq.applications.qnn.types import (
    MultipleArguments,
    SavedResult,
    ResultsCollection,
)

from classiq import execute_qnn
from classiq.synthesis import QuantumProgram


def execute(
    quantum_program: QuantumProgram, arguments: MultipleArguments
) -> ResultsCollection:
    return execute_qnn(quantum_program, arguments)


def post_process(result: SavedResult) -> torch.Tensor:
    # for example, post-processing can take some value out of `result.value.counts`, which is a `dict`
    value = _post_process_result(result)
    return torch.tensor(value)

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Execution

To facilitate the execution of your quantum layer, we supply the utility function execute_qnn. It enables you to easily execute a batch of input arguments, and instruct whether you want the sample results or the estimation results according to a specific observable. The inputs for execute_qnn are:
  • quantum_program of type QuantumProgram
  • arguments of type MultipleArguments
  • (optionally) observable of type PauliOperator.
The function returns a ResultsCollection, which is a list of SavedResult objects (see Execution Results for more information). The type of each SavedResult depends on the observable input:
  • If no observable were given, the type would be ExecutionDetails.
  • Otherwise, the type would be EstimationResult.
If only one observable was given, execute_qnn will estimate the execution of all batched arguments with this observable.If more than one observable was given, their number should match the number of batched arguments, and each execution with a set of arguments will be estimated with the matching observable.

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Examples

# Execute and return the sample results
def execute(
    quantum_program: QuantumProgram, arguments: MultipleArguments
) -> ResultsCollection:
    return execute_qnn(quantum_program, arguments)


# Execute and return the estimation results according to a specific observable
def execute(
    quantum_program: QuantumProgram, arguments: MultipleArguments
) -> ResultsCollection:
    return execute_qnn(
        quantum_program,
        arguments,
        observable=PauliOperator(
            pauli_list=[("II", 1 / 2), ("IZ", -1 / 2), ("ZI", -1 / 2)]
        ),
    )

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Behind the Scenes

Behind the scenes, the QLayer handles the following actions:
  • Processing of the PQC
  • Initializing and tracking of parameters
  • Passing the inputs and weights (as multi-dimensional tensors) to the execution function
  • Passing the results from the execution function to the post-processing function
  • Gradient calculation on the PQC