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.
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, quantum computing has been proven as an ideal theory for the design of fuzzy inference engines thanks to its capability to efficiently solve the rule explosion problem. In this scenario, the Quantum Fuzzy Inference Engine (QFIE) was proposed as a quantum algorithm able to generate an exponential computational advantage over conventional fuzzy inference engines. However, there are no practical demonstrations that QFIE can be used to efficiently manage complex systems. This paper bridges this gap by using, for the very first time, QFIE to control critical systems such as those related to particle accelerator facilities at the European Organization for Nuclear Research (CERN). As demonstrated by a series of experiments performed at the T4 target station of the CERN SPS fixed target physics beamline and at the Advanced Proton Driven Plasma Wakefield Acceleration Experiment (AWAKE), QFIE is able to efficiently control such an environment, paving the way for the use of fuzzy-enabled quantum computers in real-world applications.
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.
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.
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 computation is going to revolutionize the world of computing by enabling the design of massive parallel algorithms that solve hard problems in an efficient way, thanks to the exploitation of quantum mechanics effects, such as superposition, entanglement, and interference. These computational improvements could strongly influence the way how fuzzy systems are designed and used in contexts, such as Big Data, where computational efficiency represents a nonnegligible constraint to be taken into account. In order to pave the way toward this innovative scenario, this article introduces a novel representation of fuzzy sets and operators based on quadratic unconstrained binary optimization problems, so as to enable the implementation of fuzzy inference engines on a type of quantum computers known as quantum annealers.
Quantum computers can potentially perform certain types of optimisation problems much more efficiently than classical computers, making them a promising tool for solving complex fuzzy logic problems. In two recent developments, based on solving Quadratic Unconstrained Binary Optimization (QUBO) problems on a type of quantum computers known as quantum annealers, we have introduced novel representations of a) fuzzy sets; b) implementations of some basic fuzzy logic operators (union, intersection, alpha-cut and maximum) and; c) the centroid defuzzification. In this paper, the previous works are further extended by presenting an implementation of Mamdani inference on the quantum annealer machines. We first present how the fuzzy rules can be formulated for such an implementation, then we present how to cascade different quantum-fuzzy operators in order to implement the quantum-fuzzy inference, and finally, a sample implementation of the inference on a real quantum computer is demonstrated. Having the main components of a rule-based fuzzy logic system implemented on quantum computers, this paper provides an integrated solution for implementing a whole fuzzy rule-based system on quantum computers.