Sequence and Image Transformations with Monarq: Quantum Implementations for NISQ Devices

TL;DR

Monarq is a quantum framework that enables signal and image processing tasks on NISQ devices using novel encoding and transformation protocols.

Contribution

It introduces a unified quantum data processing framework combining QCrank encoding and EHands protocol for polynomial transformations on NISQ hardware.

Findings

Implemented quantum convolution and Fourier transform on NISQ devices.

Demonstrated edge detection and gradient computation using quantum methods.

Serves as a reference for quantum data processing applications.

Abstract

We introduce Monarq, a unified quantum data processing framework that combines QCrank encoding with the EHands protocol for polynomial transformations, and demonstrate its implementation on noisy intermediate-scale quantum (NISQ) hardware. This framework provides fundamental quantum building blocks for signal and image processing tasks, including convolution, discrete-time Fourier transform (DFT), squared gradient computation, and edge detection, serving as a reference for a broad class of data processing applications on near-term quantum devices.