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As the availability of imagery data continues to swell, so do the demands ontransmission, storage and processing power. Processing requirements to handlethis plethora of data is quickly outpacing the utility of conventionalprocessing techniques. Transitioning to quantum processing and algorithms thatoffer promising efficiencies over conventional methods can address some ofthese issues. However, to make this transformation possible, fundamental issuesof implementing real time Quantum algorithms must be overcome for crucialprocesses needed for intelligent analysis applications. For example, consideredge detection tasks which require time-consuming acquisition processes and arefurther hindered by the complexity of the devices used thus limitingfeasibility for implementation in real-time applications. Convolution isanother example of an operation that is essential for signal and imageprocessing applications, where the mathematical operations consist of anintelligent mixture of multiplication and addition that require considerablecomputational resources. This paper studies a new paired transform-basedquantum representation and computation of one-dimensional and 2-D signalsconvolutions and gradients. A new visual data representation is defined tosimplify convolution calculations making it feasible to parallelize convolutionand gradient operations for more efficient performance. The new datarepresentation is demonstrated on multiple illustrative examples for quantumedge detection, gradients, and convolution. Furthermore, the efficiency of theproposed approach is shown on real-world images.

Quantum-Inspired Edge Detection Algorithms Implementation using New Dynamic Visual Data Representation and Short-Length Convolution Computation