General information
Name
Brian Garcia Sarmina, Janeth De Anda Gil, Emmanuel Martinez Guerrero, Guo-Hua Sun, Shi-Hai Dong
Affiliation
Centro de Investigación en Computación, IPN
Demo information
Title
Q-PIPE (Quantum Gray Phase Injection for Pixel Encoding): A Practical Quantum Phase Encoding Method)
Abstract
We introduce a method for quantum image encoding that successfully bridges the gap between efficient quantum data representation and active feature extraction. By conceptualizing the state preparation phase as a parameter estimation problem, Q-PIPE leverages the quantum phase kickback mechanism to encode continuous intensity values into the relative phase and projects them directly into the computational basis. The proposed optimizing spatial traversal via a Gray-code sequence, together with the previously described mechanisms maps continuous intensity values into the computational basis with an elementary gate complexity of (O(qN)), achieving a O(log N) improvement over conventional basis encoding schemes.
Moreover, the framework systematically mitigates key readout vulnerabilities—namely phase aliasing and spectral leakage—by mapping input values to the interval [ 𝜋 , 𝜋 ] and incorporating a probability threshold formulation that scales inversely with the dimension of the spatial register.
Relevant links
Link Jupyter notebook: https://colab.research.google.com/drive/1tl-31OXOE7DG6zOhLY4sHUr9yN1vfJ8Y#scrollTo=dIYMag4_0uzm
Link paper:
General information
Name
Brian Garcia Sarmina, Janeth De Anda Gil, Emmanuel Martinez Guerrero, Guo-Hua Sun, Shi-Hai Dong
Affiliation
Centro de Investigación en Computación, IPN
Demo information
Title
Q-PIPE (Quantum Gray Phase Injection for Pixel Encoding): A Practical Quantum Phase Encoding Method)
Abstract
We introduce a method for quantum image encoding that successfully bridges the gap between efficient quantum data representation and active feature extraction. By conceptualizing the state preparation phase as a parameter estimation problem, Q-PIPE leverages the quantum phase kickback mechanism to encode continuous intensity values into the relative phase and projects them directly into the computational basis. The proposed optimizing spatial traversal via a Gray-code sequence, together with the previously described mechanisms maps continuous intensity values into the computational basis with an elementary gate complexity of (O(qN)), achieving a O(log N) improvement over conventional basis encoding schemes.
Moreover, the framework systematically mitigates key readout vulnerabilities—namely phase aliasing and spectral leakage—by mapping input values to the interval [ 𝜋 , 𝜋 ] and incorporating a probability threshold formulation that scales inversely with the dimension of the spatial register.
Relevant links
Link Jupyter notebook: https://colab.research.google.com/drive/1tl-31OXOE7DG6zOhLY4sHUr9yN1vfJ8Y#scrollTo=dIYMag4_0uzm
Link paper: