Table 3.

Algorithm design stages for quanvolutional neural network

Stage 1: An input image with small region of interest is embedded into a quantum circuit. An example of a 2 × 22 × 2 square region

Stage 2: A quantum computation, associated with a unitary matrix(Ua) in Fig. 3, is performed on the system. A Cirq could generate the unitary, most quantum operations have a unitary matrix representation applied to the gate, operations and circuit that represents an object

Stage 3: The system is then quantified by obtaining the list of classical expected values

Stage 4: Similar to the classical convolution layer, each expected value is mapped to a different channel of a single output pixel

Stage 5: The process is iteratively executing across different regions of the image. A full input image scan is viable by re-positioning an output object positioned a multi-channel image

Stage 6: The quantum convolution layer would additionally abide to quantum or classical layers

^aRefer to Abbreviations for detailed nomenclature