Quantum-parallel vectorized data encodings and computations on trapped-ions and transmons QPUs

TL;DR

This paper introduces two innovative quantum data encoding schemes, QCrank and QBArt, enabling efficient data storage and processing on quantum hardware, with demonstrated applications in pattern matching, image retrieval, and benchmarking across multiple quantum processors.

Contribution

The paper presents novel quantum data encodings, QCrank and QBArt, that enhance quantum data processing capabilities and demonstrate their effectiveness through practical algorithms and benchmarking on various QPUs.

Findings

Successful implementation of quantum algorithms for DNA pattern matching and image retrieval.

QCrank and QBArt enable high-density data encoding and efficient quantum measurements.

Benchmarking shows competitive performance across different quantum hardware platforms.

Abstract

Compact quantum data representations are essential to the emerging field of quantum algorithms for data analysis. We introduce two new data encoding schemes, QCrank and QBArt, which have a high degree of quantum parallelism through uniformly controlled rotation gates. QCrank encodes a sequence of real-valued data as rotations of the data qubits, allowing for high storage density. QBArt directly embeds a binary representation of the data in the computational basis, requiring fewer quantum measurements and lending itself to well-understood arithmetic operations on binary data. We present several applications of the proposed encodings for different types of data. We demonstrate quantum algorithms for DNA pattern matching, Hamming weight calculation, complex value conjugation, and retrieving an O(400) bits image, all executed on the Quantinuum QPU. Finally, we use various cloud-accessible QPUs, including IBMQ and IonQ, to perform additional benchmarking experiments.