Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

A Comparison of Quantum Computer Architectures in Running Fuzzy Inference Engines

Giovanni Acampora, Michele Grossi, Roberto Schiattarella

Published Aug 13, 2023• 1 citation
Publisher

Abstract

The increasing need to work in environments characterized by a large number of variables and data is driving the development of ad hoc fuzzy reasoners. Recently, the first quantum fuzzy inference engine (QFIE) has been proposed to evaluate fuzzy rules in parallel on quantum computers, whose design hardware is based on different technologies such as superconductors, photonics, trap-ion, cold atoms, and so on. Although the theoretical potential of this approach is very promising, its practical usage is still limited by the current quantum hardware, in term of dimension and noise. This paper attempts to bridge this theoretical/practical gap and pave the way for the execution of fuzzy rule-based systems on real quantum hardware by presenting a comparative study between two of the most promising quantum computing platforms, superconducting and trap-ion devices. The comparison is carried out by using QFIE for the control of a simulated particle accelerator system inspired by a real facility at the European Organization for Nuclear Research (CERN). The result of this original study allow to establish the ideal running backend for the practical use case of quantum fuzzy systems.

DOI
10.1109/fuzz52849.2023.10309673

Related papers

On the Implementation of Fuzzy Inference Engines on Quantum Computers

Giovanni Acampora, Roberto Schiattarella, Autilia Vitiello

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.

IEEE Transactions on Fuzzy Systems•Aug 29, 2022•46 citations

Distributing Fuzzy Inference Engines on Quantum Computers

Giovanni Acampora, Alfredo A. Massa, Roberto Schiattarella +1 more

Recently, a quantum algorithm called Quantum Fuzzy Inference Engine (QFIE) has been introduced with the main goal of providing exponential speedup in the execution of a Mamdani fuzzy inference engine. This quantum algorithm achieves this result by modeling a fuzzy rule base with a quantum oracle, a black box that is widely used to estimate functions using quantum mechanical principles. Although QFIE offers the possibility of performing efficient fuzzy computation on quantum computers, its real-world applicability is limited by the high levels of noise still present in current quantum computers. Consequently, it is necessary to introduce technological arrangements to make QFIE fully functional in practical cases until noise-free quantum computers are released. This paper addresses this issue by designing a distributed version of QFIE, based on the D-NISQ reference model, to distribute the computation of subsets of fuzzy rules across multiple quantum processors and minimize the negative impact of quantum noise. Experimental results prove that this distributed version of QFIE is able to significantly improve the accuracy of fuzzy computation on quantum devices, making QFIE applicable in real-world scenarios.

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)•Aug 13, 2023•7 citations

An IEEE Std 1855 Driver for Synthetizing Quantum Fuzzy Inference Engines

Giovanni Acampora, Autilia Vitiello

Over years, Fuzzy Logic established as a powerful tool for control systems as shown by the several applications both in the area of pure engineering and social, economical and political science. However, in spite of the success of fuzzy control systems, their design is originally affected by mainly two issues: the dependence on hardware and an expensive inference when the number of fuzzy variables is large. In 2016, the IEEE Std 1855 was released to address the first issue by introducing an XML-based language capable of modeling fuzzy systems in an abstract way independent from hardware. As for the second issue, recently, a quantum implementation of a fuzzy inference engine, denoted as QFIE, has been proposed to potentially provide an exponential computational advantage in the execution of fuzzy rules. This paper aims at integrating IEEE Std 1855 and QFIE to provide the fuzzy community with controllers transparent with respect to hardware and efficient in terms of fuzzy rule execution. This integration is achieved by introducing an IEEE Std 1855 driver capable of converting transparent fuzzy systems in controllers executable on quantum computers. As shown in a case study, this driver enables quantum implementation of a fuzzy system without knowledge of quantum hardware and technologies.

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)•Aug 13, 2023•1 citation

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

G. Nunziata, Serena Crisci, G. De Gregorio +4 more

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.

Applied Soft Computing•Sep 10, 2025•4 citations

Quantum Implementation of Fuzzy Systems through Grover’s Algorithm

Giovanni Acampora, F. Luongo, Autilia Vitiello

This paper introduces the first implementation of fuzzy rule-based systems for quantum computers. Quantum computers are incredibly powerful machines that use principles of quantum mechanics, such as superposition and entanglement, to process information in a more efficient way than classical computers. Currently, several companies and academic research groups are focusing their efforts on the development of quantum algorithms for artificial intelligence but, so far, no significant contribution in the area of the fuzzy inference for quantum machines has been proposed. This article bridges this gap by introducing a quantum implementation of a fuzzy rule-based system through a synergistic usage of the well-known Grover's algorithm and look-up tables. As shown by a case study, the behaviour of the quantum implementation of the fuzzy rule-based system is comparable to its classical version, but with a quadratic asymptotic speed-up than a fuzzy rule-based system implemented by a classical look-up table.

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)•Jul 1, 2018•12 citations

Using Quantum Fuzzy Inference Engine for Crime Prediction

Giovanni Acampora, Roberto Schiattarella, Autilia Vitiello

This paper presents the first application of a quantum Fuzzy Rule-Based System (FRBS) to the problem of crime rate prediction using socio-economic and law enforcement features. Crime prediction typically depends on a large number of factors that leads to the design and execution of numerous fuzzy rules. The use of a quantum approach, named Quantum Fuzzy Inference Engine (QFIE), offers a computationally efficient solution, leveraging quantum parallelism to evaluate a large number of rules exponentially faster than classical systems. Moreover, as shown in the experimental session involving a well-known crime dataset, QFIE-based approach achieves slightly better results than classical counterpart in terms of the root mean square error and the coefficient of determination. These findings underscore the promise of quantum inference mechanisms in advancing fuzzy logic systems, paving the way for their future application in computationally intensive real-world scenarios as quantum hardware matures.

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)•Jul 6, 2025•1 citation
The increasing need to work in environments characterized by a large number of variables and data is driving the development of ad hoc fuzzy reasoners. Recently, the first quantum fuzzy inference engine (QFIE) has been proposed to evaluate fuzzy rules in parallel on quantum computers, whose design hardware is based on different technologies such as superconductors, photonics, trap-ion, cold atoms, and so on. Although the theoretical potential of this approach is very promising, its practical usage is still limited by the current quantum hardware, in term of dimension and noise. This paper attempts to bridge this theoretical/practical gap and pave the way for the execution of fuzzy rule-based systems on real quantum hardware by presenting a comparative study between two of the most promising quantum computing platforms, superconducting and trap-ion devices. The comparison is carried out by using QFIE for the control of a simulated particle accelerator system inspired by a real facility at the European Organization for Nuclear Research (CERN). The result of this original study allow to establish the ideal running backend for the practical use case of quantum fuzzy systems.
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.
Recently, a quantum algorithm called Quantum Fuzzy Inference Engine (QFIE) has been introduced with the main goal of providing exponential speedup in the execution of a Mamdani fuzzy inference engine. This quantum algorithm achieves this result by modeling a fuzzy rule base with a quantum oracle, a black box that is widely used to estimate functions using quantum mechanical principles. Although QFIE offers the possibility of performing efficient fuzzy computation on quantum computers, its real-world applicability is limited by the high levels of noise still present in current quantum computers. Consequently, it is necessary to introduce technological arrangements to make QFIE fully functional in practical cases until noise-free quantum computers are released. This paper addresses this issue by designing a distributed version of QFIE, based on the D-NISQ reference model, to distribute the computation of subsets of fuzzy rules across multiple quantum processors and minimize the negative impact of quantum noise. Experimental results prove that this distributed version of QFIE is able to significantly improve the accuracy of fuzzy computation on quantum devices, making QFIE applicable in real-world scenarios.
Over years, Fuzzy Logic established as a powerful tool for control systems as shown by the several applications both in the area of pure engineering and social, economical and political science. However, in spite of the success of fuzzy control systems, their design is originally affected by mainly two issues: the dependence on hardware and an expensive inference when the number of fuzzy variables is large. In 2016, the IEEE Std 1855 was released to address the first issue by introducing an XML-based language capable of modeling fuzzy systems in an abstract way independent from hardware. As for the second issue, recently, a quantum implementation of a fuzzy inference engine, denoted as QFIE, has been proposed to potentially provide an exponential computational advantage in the execution of fuzzy rules. This paper aims at integrating IEEE Std 1855 and QFIE to provide the fuzzy community with controllers transparent with respect to hardware and efficient in terms of fuzzy rule execution. This integration is achieved by introducing an IEEE Std 1855 driver capable of converting transparent fuzzy systems in controllers executable on quantum computers. As shown in a case study, this driver enables quantum implementation of a fuzzy system without knowledge of quantum hardware and technologies.
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.
This paper introduces the first implementation of fuzzy rule-based systems for quantum computers. Quantum computers are incredibly powerful machines that use principles of quantum mechanics, such as superposition and entanglement, to process information in a more efficient way than classical computers. Currently, several companies and academic research groups are focusing their efforts on the development of quantum algorithms for artificial intelligence but, so far, no significant contribution in the area of the fuzzy inference for quantum machines has been proposed. This article bridges this gap by introducing a quantum implementation of a fuzzy rule-based system through a synergistic usage of the well-known Grover's algorithm and look-up tables. As shown by a case study, the behaviour of the quantum implementation of the fuzzy rule-based system is comparable to its classical version, but with a quadratic asymptotic speed-up than a fuzzy rule-based system implemented by a classical look-up table.
This paper presents the first application of a quantum Fuzzy Rule-Based System (FRBS) to the problem of crime rate prediction using socio-economic and law enforcement features. Crime prediction typically depends on a large number of factors that leads to the design and execution of numerous fuzzy rules. The use of a quantum approach, named Quantum Fuzzy Inference Engine (QFIE), offers a computationally efficient solution, leveraging quantum parallelism to evaluate a large number of rules exponentially faster than classical systems. Moreover, as shown in the experimental session involving a well-known crime dataset, QFIE-based approach achieves slightly better results than classical counterpart in terms of the root mean square error and the coefficient of determination. These findings underscore the promise of quantum inference mechanisms in advancing fuzzy logic systems, paving the way for their future application in computationally intensive real-world scenarios as quantum hardware matures.