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Memristor Crossbar Arrays Performing Quantum Algorithms.

Authors :
Fyrigos, Iosif-Angelos
Ntinas, Vasileios
Vasileiadis, Nikolaos
Sirakoulis, Georgios Ch.
Dimitrakis, Panagiotis
Zhang, Yue
Karafyllidis, Ioannis G.
Source :
IEEE Transactions on Circuits & Systems. Part I: Regular Papers. Feb2022, Vol. 69 Issue 2, p552-563. 12p.
Publication Year :
2022

Abstract

There is a growing interest in quantum computers and quantum algorithm development. It has been proved that ideal quantum computers, with zero error rates and large decoherence times, can solve problems that are intractable for today’s classical computers. Quantum computers use two resources, superposition and entanglement, that have no classical analog. Since quantum computer platforms that are currently available comprise only a few dozen of qubits, the use of quantum simulators is essential in developing and testing new quantum algorithms. We present a novel quantum simulator based on memristor crossbar circuits and use them to simulate well-known quantum algorithms, namely the Deutsch and Grover quantum algorithms. In quantum computing the dominant algebraic operations are matrix-vector multiplications. The execution time grows exponentially with the simulated number of qubits, causing an exponential slowdown in quantum algorithm execution using classical computers. In this work, we show that the inherent characteristics of memristor arrays can be used to overcome this problem and that memristor arrays can be used not only as independent quantum simulators but also as a part of a quantum computer stack where classical computers accelerators are connected. Our memristive crossbar circuits are re-configurable and can be programmed to simulate any quantum algorithm. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15498328
Volume :
69
Issue :
2
Database :
Academic Search Index
Journal :
IEEE Transactions on Circuits & Systems. Part I: Regular Papers
Publication Type :
Periodical
Accession number :
154974607
Full Text :
https://doi.org/10.1109/TCSI.2021.3123575