Quantum fuzzy logic for edge detection: A demonstration on NISQ hardware

Quantum computing offers the potential to enhance computational efficiency beyond classical methods, but practical implementation remains challenging due to the limitations of Noisy Intermediate-Scale Quantum (NISQ) hardware, namely, restricted qubit counts, limited connectivity, and the presence of noise and decoherence. This study presents a novel approach to edge detection by leveraging a recently developed Quantum Fuzzy Inference Engine, implemented on a NISQ device. We introduce an optimized quantum circuit for its implementation, reducing qubit requirements and gate depth to improve execution on NISQ hardware. To overcome constraints related to large-scale image processing, a hybrid quantum–classical lookup table approach is employed. Edge detection performance is evaluated on the Berkeley Segmentation Data Set and Benchmarks 500 dataset under different conditions, including classical execution, ideal quantum simulation, noisy quantum simulation, and NISQ hardware calculation. Results demonstrate that the quantum fuzzy logic-based edge detection achieves outcomes comparable to classical methods by using fewer operations, marking a step toward practical quantum-enhanced image processing. • A Quantum Fuzzy Inference Engine (QFIE) for edge detection is proposed and implemented on Noisy Intermediate Scale Quantum (NISQ) hardware. • An optimized QFIE circuit reduces qubit count and gate depth, enhancing suitability for NISQ devices. • A hybrid quantum–classical approach utilizing a lookup table efficiently processes image gradients, addressing NISQ hardware constraints. • Experimental validation on the BSDS-500 dataset shows that the quantum approach achieves results comparable to classical fuzzy logic-based edge detection even using NISQ devices.