FEATURE EMBEDDING IN QUANTUM DISCRETE TRANSFORM FOR OBJECT DETECTION AND CLASSIFICATION USING SENSOR DATA

Abstract

Abstract: Object detection and classification in the data collected from the sensor networks is challenging task. This paper presents novel mechanisms Quantum Fourier Transform (QFT) and Quantum Discrete Transform (QDT) which can be utilized for object detection and classification using sensor data. The quantum Fourier transform with its mathematical modelling and example is described in this paper. The QFT is utilized to propose quantum Fourier transform sampler algorithm. The mathematical elaboration with example is elaborated. An overview of proposed quantum discrete transform with proposed quantum discrete transform algorithm, and proposed designed quantum circuit for quantum discrete transform are described in this paper. The proposed quantum Fourier transform sampler algorithm is evaluated using the open source Qiskit platform. The evaluation of the algorithm is also extended to the real quantum hardware IBM Quantum by utilizing the IBM quantum API. For the evaluation of the proposed approach on the real quantum hardware the inverse QFT is utilized.