Quantum Computers as Fuzzy Computers

Sovan Samanta, Leo Mršić, Tofigh Allahviranloo

Bart D’Hooghe, Jarosław Pykacz, Romàn R. Zapatrin

Cesarino Bertini, Roberto Leporini

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.

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.

David Peter Divincenzo, Daniel Loss