PALQA: A Novel Parameterized Position-Aware Lossy Quantum Autoencoder using LSB Control Qubit for Efficient Image Compression

TL;DR

This paper introduces PALQA, a quantum autoencoder that uses position-aware control qubits for efficient image compression, outperforming classical JPEG and existing quantum autoencoders in gate efficiency and image quality.

Contribution

It presents a novel parameterized lossy quantum autoencoder utilizing position-aware control qubits and a transformed coefficient approach for scalable image compression.

Findings

Superior PSNR performance compared to JPEG and other quantum autoencoders

Reduced gate complexity in the quantum circuit

Effective compression across various image resolutions

Abstract

With the growing interest in quantum computing, quantum image processing technology has become a vital research field due to its versatile applications and ability to outperform classical computing. A quantum autoencoder approach has been used for compression purposes. However, existing autoencoders are limited to small-scale images, and the mechanisms of state compression remain unclear. There is also a need for efficient quantum autoencoders using standard representation approaches and for studying parameterized position-aware control qubits and their corresponding quality measurement metrics. This work introduces a novel parameterized position-aware lossy quantum autoencoder (PALQA) circuit that utilizes the least significant bit control qubit for image compression. The PALQA circuit employs a transformed coefficient block-based modified state connection approach to efficiently compress images at various resolutions. The method leverages compression opportunities in the state-label connection by applying position-aware least significant control qubit. Compared to JPEG and other enhanced quantum representation-based quantum autoencoders, the PALQA circuit demonstrates superior performance in terms of the number of gates required and PSNR metrics.