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 non-negligible constraint to be taken into account. In order to pave the way towards this innovative scenario, this paper introduces a novel representation of fuzzy sets and operators based on Quadratic Unconstrained Binary Optimization (QUBO) problems, so as to enable the implementation of fuzzy inference engines on a type of quantum computers known as quantum annealers.

Fuzzy Logic on Quantum Annealers / Acampora, G.; Pourabdollah, A.; Schiattarella, R.. - In: IEEE TRANSACTIONS ON FUZZY SYSTEMS. - ISSN 1063-6706. - 30:8(2022), pp. 3389-3394. [10.1109/TFUZZ.2021.3113561]

Fuzzy Logic on Quantum Annealers

Acampora G.;Schiattarella R.
2022

Abstract

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 non-negligible constraint to be taken into account. In order to pave the way towards this innovative scenario, this paper introduces a novel representation of fuzzy sets and operators based on Quadratic Unconstrained Binary Optimization (QUBO) problems, so as to enable the implementation of fuzzy inference engines on a type of quantum computers known as quantum annealers.
2022
Fuzzy Logic on Quantum Annealers / Acampora, G.; Pourabdollah, A.; Schiattarella, R.. - In: IEEE TRANSACTIONS ON FUZZY SYSTEMS. - ISSN 1063-6706. - 30:8(2022), pp. 3389-3394. [10.1109/TFUZZ.2021.3113561]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/881529
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