Evaluating Quantum Encoding Strategies for Face Biometrics: Performance, Complexity, and Implementation
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Abstract
The quantum data encoding techniques enable us to translate the classical data into the quantum states so that quantum algorithms and operations can be performed on the dataset. This paper identifies the key parameters which help to decide the most suitable data encoding technique for quantum-based face biometric systems. In this paper, four popular quantum data encoding approaches are considered: basic encoding, amplitude encoding, angle encoding and Hamiltonian encoding. Each method is evaluated based on its ability to handle high-dimensional facial data, circuit complexity, resource requirements, and potential quantum advantages. The paper also presents performance evaluations of these encoding techniques across specific face biometric tasks including face detection, verification, expression recognition, and facial data search. The study found that the amplitude encoding is most practical approach for near-term quantum face biometric systems. It concludes by identifying key challenges in quantum biometric systems and suggesting future research directions.
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References
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