Quantum Artificial Intelligence Predictions Enhancement by Improving Signal Processing
Poster
Paper/Presentation Title | Quantum Artificial Intelligence Predictions Enhancement by Improving Signal Processing |
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Presentation Type | Poster |
Authors | Riaz, Farina (Author), Abdulla, Shahab (Author), Ni, Wei (Author), Radfar, Mohsen (Author), Deo, Ravinesh (Author) and Hopkins, Susan (Author) |
Year | 2022 |
Place of Publication | Toowoomba, Australia |
Digital Object Identifier (DOI) | https://doi.org/10.13140/RG.2.2.34754.66245 |
Web Address (URL) of Paper | https://research.csiro.au/qt/quantum-australia-2022/ |
Conference/Event | Quantum Australia Conference 2022 |
Event Details | Quantum Australia Conference 2022 Event Date 23 to end of 25 Feb 2022 Event Location Online Sydney Quantum Academy |
Abstract | Quantum computers have a great potential to change the future of Artificial Intelligence (AI). Although classical supercomputers have powerful processing systems and are efficient for AI applications, the processing speed limit in existing computer systems is still a challenge. Quantum computers (QC) are inspired from nature, that exhibits quantum phenomena of the Superposition and Entanglement. Algorithms designed for QC like Shor’s and Groover have achieved polynomial speed over classical computers. This has attracted many researchers worldwide to investigate the problem and to design more robust algorithms for QC, that are challenge for classical computer. Intelligent Transportation System (ITS) has recently attracted many researchers, to develop fast smart vehicles and smart traffic systems. Reliable, accurate and timely prediction is a major goal of any AI application like traffic flow prediction and delay in predictions can cause unfavourable results. QCs have potential to process huge amount of data for timely prediction. AI deep learning algorithms e.g., Neural Networks (NN), can deal with the processing of complex image data and time series signals. |
Keywords | Quantum computing, Quantum Artificial Intelligence, Signal Processing |
Related Output | |
Is part of | Coloured image classification with quantum machine learning algorithms for intelligent transportation systems |
ANZSRC Field of Research 2020 | 510805. Quantum technologies |
461103. Deep learning | |
Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
Byline Affiliations | University of Southern Queensland |
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia | |
La Trobe University | |
School of Mathematics, Physics and Computing | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q754w/quantum-artificial-intelligence-predictions-enhancement-by-improving-signal-processing
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