In this work, we explore a novel edge detection method based on a Quantum Fuzzy Inference Engine implemented on a real quantum computer. We address an industrial defect detection problem using a proprietary fruit dataset. Our approach combines a hybrid quantum-classical lookup table for edge detection with a classical contour tracking algorithm and is compared against state-of-the-art classical methods. Our hybrid model achieves comparable precision to its classical counterpart while requiring fewer operations. Moreover, comparisons with other leading classical techniques confirm the reliability of our approach, as it delivers very similar results.
In this work, we explore a novel edge detection method based on a Quantum Fuzzy Inference Engine implemented on a real quantum computer. We address an industrial defect detection problem using a proprietary fruit dataset. Our approach combines a hybrid quantum-classical lookup table for edge detection with a classical contour tracking algorithm and is compared against state-of-the-art classical methods. Our hybrid model achieves comparable precision to its classical counterpart while requiring fewer operations. Moreover, comparisons with other leading classical techniques confirm the reliability of our approach, as it delivers very similar results.
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.
Abstract Quantum computing is being explored to tackle complex problems, such as image processing in this specific case. The ability of qubits to exist in multiple states at once and take advantage of quantum phenomena such as superposition and entanglement could have significant applications in a variety of fields, including image processing. Understandably, at this early stage in the development of quantum computing, quantum algorithms are not yet as efficient as classical algorithms for certain tasks. However, as quantum technology advances and becomes more accessible, we are likely to see significant improvements in terms of efficiency and problem-solving capabilities. The use of quantum computing to process images and detect specific features, such as the body contour of sheep for weight assessment or disease detection, has the potential to be a valuable tool in the agriculture and livestock industry. In this paper, we present the results of a quantum edge detector by analysing how the resolution affects the image result. Thus, a quantum edge detection model is implemented in the quantum simulator to analyse the edge recovery capability of an image as a function of the number of pixels in order to test the efficiency of quantum computers for image processing. The experiment results illustrate that edge detection times using quantum algorithms exceed 9000 seconds on 128x128 pixel images. Although the results are worse than those obtained using classical image processing models, they are promising since a resolution of 64$\times 64or128\times $128 is precise enough to determine the body contour of the sheep and can be executed in a quantum simulator. With the evolution of quantum computers, we are approaching the possibility of being able to use them for image processing tasks.
The application of quantum computing to the field of image processing has produced several promising applications: quantum image representation techniques have been developed showing how, by taking advantage of quantum properties like entanglement and superposition, many image processing algorithms could have an exponential speed-up in comparison to their "classical" counterparts. In this paper, after briefly discussing some of the main quantum image representation methods, we propose an improved version of a quantum edge detection algorithm.
As Quantum Machine Learning (QML) continues to mature, there is an increasing demand for novel architectures that broaden the ways quantum models learn from data, represent knowledge, and perform inference. In this work, we address this need by introducing a hybrid Quantum Fuzzy Cognitive Map (QFCM) model for pattern recognition, in which the core arithmetic operations of the classical Fuzzy Cognitive Map (FCM) inference mechanism are replaced by a parameterized quantum circuit. The proposed model preserves the structural foundations and causal inference principles of classical FCMs, while leveraging quantum computation to model and process interactions among concepts. The proposed QFCM was evaluated on six benchmark datasets, demonstrating competitive performance and achieving a 9.37% increase in prediction confidence compared to the classical FCM. Overall, this work contributes to the growing QML ecosystem by embedding the fundamental principles of FCMs into a quantum circuit–based formulation, thereby extending their representational and computational capabilities.
Quantum computers can be a revolutionary tool to implement inference engines for fuzzy rule-based systems. In fact, the use of quantum mechanical principles can enable parallel execution of fuzzy rules and allow them to be used efficiently in complex contexts such as distributed and big data environments. This article introduces the very first quantum-based fuzzy inference engine that is capable of providing exponential acceleration in fuzzy rule execution compared to its classical counterpart, and allows a quantum computer to be programmed by fuzzy linguistic rules. The proposed inference engine was implemented using a quantum algorithm design scheme based on the oracle notion. This scheme allows the modeling of a fuzzy rule-based system as a Boolean function, the oracle, which is able to reconstruct the relationships between the antecedent and consequent parts of fuzzy rules, and can be efficiently computed on a quantum computer. The suitability of the proposed quantum algorithm for use as a fuzzy inference engine was tested in a typical benchmark scenario, such as that provided by inverted pendulum control.
Classical edge detection algorithms often struggle to process large, high-resolution image datasets efficiently. Quantum image processing offers a promising alternative, but current implementations face significant challenges, such as time-consuming data acquisition, complex device requirements, and limited real-time processing capabilities. This work presents a novel paired transform-based quantum representation for efficient image processing. This representation enables the parallelization of convolution operations, simplifies gradient calculations, and facilitates the processing of one-dimensional and two-dimensional signals. We demonstrate that our approach achieves improved processing speed compared to classical methods while maintaining comparable accuracy. The successful implementation of real-world images highlights the potential of this research for large-scale quantum image processing, architecture-specific optimizations, and applications beyond edge detection.