Quantum-parallel vectorized data encodings and computations on trapped-ion and transmon QPUs.

Autor: Balewski J; National Energy Research Scientific Computing Center, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA., Amankwah MG; National Energy Research Scientific Computing Center, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.; Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH, 44106, USA., Van Beeumen R; Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA., Bethel EW; Computer Science Department, San Francisco State University, San Francisco, CA, 94132, USA., Perciano T; Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. tperciano@lbl.gov., Camps D; National Energy Research Scientific Computing Center, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. dcamps@lbl.gov.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Feb 10; Vol. 14 (1), pp. 3435. Date of Electronic Publication: 2024 Feb 10.
DOI: 10.1038/s41598-024-53720-x
Abstrakt: Compact data representations in quantum systems are crucial for the development of quantum algorithms for data analysis. In this study, we present two innovative data encoding techniques, known as QCrank and QBArt, which exhibit significant quantum parallelism via uniformly controlled rotation gates. The QCrank method encodes a series of real-valued data as rotations on data qubits, resulting in increased storage capacity. On the other hand, QBArt directly incorporates a binary representation of the data within the computational basis, requiring fewer quantum measurements and enabling well-established arithmetic operations on binary data. We showcase various applications of the proposed encoding methods for various data types. Notably, we demonstrate quantum algorithms for tasks such as DNA pattern matching, Hamming weight computation, complex value conjugation, and the retrieval of a binary image with 384 pixels, all executed on the Quantinuum trapped-ion QPU. Furthermore, we employ several cloud-accessible QPUs, including those from IBMQ and IonQ, to conduct supplementary benchmarking experiments.
(© 2024. The Author(s).)
Databáze: MEDLINE
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