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

Jan Balewski1, Mercy G Amankwah1,2, Roel Van Beeumen3

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Two new quantum data encoding methods, QCrank and QBArt, enhance quantum parallelism for data analysis. These techniques improve storage and enable efficient quantum algorithms for tasks like DNA matching and image retrieval.

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Area of Science:

  • Quantum Information Science
  • Quantum Computing
  • Data Encoding

Background:

  • Compact data representations are essential for advancing quantum algorithms in data analysis.
  • Developing efficient methods to store and process data on quantum systems is a key challenge.

Purpose of the Study:

  • To introduce two novel quantum data encoding techniques: QCrank and QBArt.
  • To demonstrate the effectiveness of these methods in enhancing quantum parallelism and enabling diverse quantum algorithms.

Main Methods:

  • QCrank encodes real-valued data as qubit rotations, increasing storage capacity.
  • QBArt uses binary representations within the computational basis for fewer measurements and direct arithmetic operations.
  • Uniformly controlled rotation gates are utilized to achieve quantum parallelism.

Main Results:

  • The proposed methods were applied to various data types, including DNA pattern matching, Hamming weight computation, and complex value conjugation.
  • A 384-pixel binary image retrieval task was successfully executed on a trapped-ion quantum processing unit (QPU).
  • Benchmarking experiments were conducted on multiple cloud-accessible QPUs from IBMQ and IonQ.

Conclusions:

  • QCrank and QBArt offer significant advancements in quantum data representation and processing.
  • These encoding techniques facilitate the development of practical quantum algorithms for real-world data analysis tasks.
  • Experimental validation on different QPUs confirms the viability and performance of the proposed methods.