Amankwah Mercy G, Camps Daan, Bethel E Wes, Van Beeumen Roel, Perciano Talita
Lawrence Berkeley National Laboratory, Computing Sciences Area, Berkeley, CA, 94720, USA.
Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH, USA.
Sci Rep. 2022 May 11;12(1):7712. doi: 10.1038/s41598-022-11024-y.
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 scales linearly in the number of pixels and does not use ancilla qubits. Furthermore, the circuits only consist of [Formula: see text] gates and [Formula: see text] 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.
我们引入了一种新颖且统一的量子像素表示框架，该框架涵盖了近期文献中提出的许多最流行的表示方法，例如（I）FRQI、（I）NEQR、MCRQI和（I）NCQI。所提出的QPIXL框架带来了更高效的电路实现，并显著降低了所有考虑的量子像素表示的门复杂度。我们的方法在像素数量上呈线性扩展，并且不使用辅助量子比特。此外，这些电路仅由[公式：见原文]门和[公式：见原文]门组成，使其在噪声中等规模量子（NISQ）时代具有实用性。此外，我们提出了一种电路和图像压缩算法，该算法被证明非常有效，例如对于科学图像，在不牺牲图像质量的情况下，能够将制备FRQI态所需的门减少多达90%。我们的算法作为QPIXL++（一个量子图像像素库）的一部分公开提供。
