Qconv

Qconv is an acronym used in multiple domains, and there is no single, universal definition. In practice, it most often refers to two distinct concepts: quantum convolution-related ideas in quantum computing and quantum machine learning, and quantized convolution in neural networks.

In quantum computing and quantum machine learning, qconv can appear as shorthand for quantum convolution operations or quantum convolutional neural networks (QCNNs). QCNNs are inspired by classical convolutional neural networks but operate on quantum data using parameterized quantum circuits. The idea is to process information encoded in quantum states through hierarchical layers that resemble convolution and pooling, with measurements used to extract a final classical decision. Proponents argue QCNNs may harness quantum resources to capture correlations in high-dimensional quantum data, while challenges include hardware noise and the current limitations of quantum processors.

In machine learning and deep learning, qconv often denotes quantized convolution, a technique used in quantized neural networks. Here the weights and/or activations are represented with low-precision integers (for example 8-bit or fewer), reducing memory usage and accelerating inference on compatible hardware. Quantized convolution is typically achieved through quantization-aware training or post-training quantization to maintain accuracy while benefiting from efficiency gains.

Other uses of the term are less standardized, and some authors may employ qconv as shorthand in different contexts. See also quantized neural networks and quantum convolutional neural networks for related concepts.