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Quantum Discrete Transform for Real-Time Object Detection in Today's Smart Era

Quantum Discrete Transform for Real-Time Object Detection in Today's Smart Era

Amol Prakash Bhagat, Sandip Kendre
Copyright: © 2023 |Pages: 13
ISBN13: 9781668466971|ISBN10: 166846697X|EISBN13: 9781668466988
DOI: 10.4018/978-1-6684-6697-1.ch010
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MLA

Bhagat, Amol Prakash, and Sandip Kendre. "Quantum Discrete Transform for Real-Time Object Detection in Today's Smart Era." Handbook of Research on Quantum Computing for Smart Environments, edited by Amit Kumar Tyagi, IGI Global, 2023, pp. 178-190. https://doi.org/10.4018/978-1-6684-6697-1.ch010

APA

Bhagat, A. P. & Kendre, S. (2023). Quantum Discrete Transform for Real-Time Object Detection in Today's Smart Era. In A. Tyagi (Ed.), Handbook of Research on Quantum Computing for Smart Environments (pp. 178-190). IGI Global. https://doi.org/10.4018/978-1-6684-6697-1.ch010

Chicago

Bhagat, Amol Prakash, and Sandip Kendre. "Quantum Discrete Transform for Real-Time Object Detection in Today's Smart Era." In Handbook of Research on Quantum Computing for Smart Environments, edited by Amit Kumar Tyagi, 178-190. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-6697-1.ch010

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Abstract

The application of quantum technology for remote sensing has been considered for at least the last 20 years. An active imaging information transmission technology for satellite-borne quantum remote sensing is proposed, providing solutions and a technical basis for realizing active imaging technology relying on quantum mechanics principles. Quantum technology is also used in interferometric synthetic aperture radars. A residue connection problem in the phase unwrapping procedure as quadratic unconstrained binary optimization problem can be solved by using the D-Wave quantum annealer. A quantum annealer application has been explored in the past for subset feature selection and the classification of hyperspectral images. In this chapter, quantum discrete transform is proposed and analyzed, which can be used for real-time object detection in distinct fields.

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