Quantum CNN (QCNN) Scaling Study

This page details the empirical complexities and scalability constraints of the Quantum Convolutional Neural Network (QCNN) implementation in MerLin.

Complexity and Scaling

After conducting a rigorous scaling study of the QCNN architecture, we have mapped its computational complexity along two main axes:

Batch Size: The computational complexity scales linearly with respect to the batch_size.
Input Dimensions: The complexity scales exponentially with respect to the input dimensions.

Execution Constraints and Safeguards

To ensure execution stability and prevent runtime failures, specific limits must be observed during configuration:

Batch Size: The batch_size can safely be scaled beyond 100, as its overall impact on performance overhead remains minor.
Input Dimension: The input dimension is not capped, however, we recommend

the user to avoid going above 24 if it is possible.

Here are the different graphs representing the scaling:

QCNN scaling study graphs — **Figure:** Graph scaling study.

Running the Benchmark Study

To reproduce the scaling study or evaluate performance updates under this subsystem, navigate to the merlin repository and execute the following benchmark pipeline:

python -m benchmarks.QCNN_scaling_study_benchmark
python -m benchmarks.scaling_study_graphs

For structural architecture details and QCNN layer signatures, see the internal expert documentation section: merlin.models.qcnn. See also: merlin.models.qcnn.QCNNClassifier