Quantum pixel representations and compression for N-dimensional images

Journal Article · 10 May 2022 · Scientific Reports

DOI: https://doi.org/10.1038/s41598-022-11024-y · OSTI ID:1867314

Amankwah, Mercy G.; Camps, Daan; Bethel, E. Wes; Van Beeumen, Roel; Perciano, Talita

Abstract 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 $$$$R_y$$$$ $R_y$ gates and $$$$\text {CNOT}$$$$ $\text{CNOT}$ 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 , a Quantum Image Pixel Library.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1867314
• Journal Information: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 12; ISSN 2045-2322
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United Kingdom
• Language: English