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

Journal Article · 09 February 2024 · Scientific Reports

DOI: https://doi.org/10.1038/s41598-024-53720-x · OSTI ID:2319218

Balewski, Jan ^[1]; Amankwah, Mercy G. ^[2]; Van Beeumen, Roel ^[3]; Bethel, E. Wes ^[4]; Perciano, Talita ^[3]; Camps, Daan ^[1]

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1. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
2. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Case Western Reserve Univ., Cleveland, OH (United States)
3. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
4. San Francisco State Univ., CA (United States)

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.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
• Grant/Contract Number: AC02-05CH11231; AC05-00OR22725
• OSTI ID: 2319218
• Alternate ID(s): OSTI ID: 2332881
• Journal Information: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 14; ISSN 2045-2322
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United States
• Language: English