Quantum pixel representations and compression for $N$-dimensional images
This work addresses the problem of inefficient quantum image processing for researchers in quantum computing, offering incremental improvements by unifying existing methods and enhancing practicality in the NISQ era.
The authors introduced the QPIXL framework to unify quantum pixel representations, reducing gate complexity and enabling more efficient circuit implementations without ancilla qubits, and demonstrated a compression algorithm that cuts gates by up to 90% for example images without quality loss.
We introduce a novel and uniform framework for quantum pixel representations that overarches many of the most popular representations proposed in the recent literature, such as (I)FRQI, (I)NEQR, MCRQI, and (I)NCQI. The proposed QPIXL framework results in more efficient circuit implementations and significantly reduces the gate complexity for all considered quantum pixel representations. Our method only requires a linear number of gates in terms of the number of pixels and does not use ancilla qubits. Furthermore, the circuits only consist of Ry gates and CNOT gates making them practical in the NISQ era. Additionally, we propose a circuit and image compression algorithm that is shown to be highly effective, being able to reduce the necessary gates to prepare an FRQI state for example scientific images by up to 90% without sacrificing image quality. Our algorithms are made publicly available as part of QPIXL++, a Quantum Image Pixel Library.