Quantum Super-resolution by Adaptive Non-local Observables

TL;DR

This paper introduces a novel quantum machine learning framework using Variational Quantum Circuits with adaptive measurements, achieving significant super-resolution improvements with smaller models.

Contribution

It is the first to explore quantum circuits for super-resolution, proposing adaptive non-local observables to enhance resolution beyond classical methods.

Findings

Achieves up to five-fold higher resolution

Uses smaller quantum models for effective super-resolution

Demonstrates potential of quantum circuits in image processing

Abstract

Super-resolution (SR) seeks to reconstruct high-resolution (HR) data from low-resolution (LR) observations. Classical deep learning methods have advanced SR substantially, but require increasingly deeper networks, large datasets, and heavy computation to capture fine-grained correlations. In this work, we present the \emph{first study} to investigate quantum circuits for SR. We propose a framework based on Variational Quantum Circuits (VQCs) with \emph{Adaptive Non-Local Observable} (ANO) measurements. Unlike conventional VQCs with fixed Pauli readouts, ANO introduces trainable multi-qubit Hermitian observables, allowing the measurement process to adapt during training. This design leverages the high-dimensional Hilbert space of quantum systems and the representational structure provided by entanglement and superposition. Experiments demonstrate that ANO-VQCs achieve up to five-fold higher resolution with a relatively small model size, suggesting a promising new direction at the intersection of quantum machine learning and super-resolution.