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

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.