Quantum Image Visualizer: Visual Debugging of Quantum Image Processing Circuits

TL;DR

This work tackles the debugging bottleneck in quantum image processing (QIMP) by introducing Quantum Image Visualizer (QIV), an interactive tool that visualizes NEQR-encoded images as they evolve under quantum circuits. Built atop full statevector simulation, QIV uses explicit encoding with juxtaposition across gates and pixel-focused views to reveal how gate operations affect per-pixel color distributions, enabling stepwise inspection and error detection. The design study with eight domain experts yields four design requirements and a multi-view implementation, including Image View, Modality View, Variation View, Color Probability View, and Difference Views, demonstrated through a case study and a qualitative user study. Results show that QIV improves understanding and debugging of QIMP algorithms, while also highlighting a learning curve and opportunities to extend to larger images, 3D data, and auxiliary qubits for broader applicability and hardware relevance.

Abstract

Quantum computing is an emerging field that utilizes the unique principles of quantum mechanics to offer significant advantages in algorithm execution over classical approaches. This potential is particularly promising in the domain of quantum image processing, which aims to manipulate all pixels simultaneously. However, the process of designing and verifying these algorithms remains a complex and error-prone task. To address this challenge, new methods are needed to support effective debugging of quantum circuits. The Quantum Image Visualizer is an interactive visual analysis tool that allows for the examination of quantum images and their transformation throughout quantum circuits. The framework incorporates two overview visualizations that trace image evolution across a sequence of gates based on the most probable outcomes. Interactive exploration allows users to focus on relevant gates, and select pixels of interest. Upon selection, detailed visualizations enable in-depth inspection of individual pixels and their probability distributions, revealing how specific gates influence the likelihood of pixel color values and the magnitude of these changes. An evaluation of the Quantum Image Visualizer was conducted through in-depth interviews with eight domain experts. The findings demonstrate the effectiveness and practical value of our approach in supporting visual debugging of quantum image processing circuits.

Figures (7)

• Figure 1: (A) Visual representation of the image encoded in Eq. \ref{['eq:neqr_img_example']}. (B) The probability distributions of the color values (blue) induced by the NEQR scheme for each pixel position (red) of the image.
• Figure 2: (A) Circuit preparing and processing the image in \ref{['fig:background_neqr_explanation']}. (B) Image View: Gate labels on the left, followed by the image representation before and after the gate was applied, and finally the Modality View.
• Figure 3: Variation View positioned in between the grayscale images. It shows the departure of the distributions for each pixel. (A) and (B) show filtering through user interaction.
• Figure 4: Color Probability View visualizing the color probability distribution accumulated in eight bins of all four selected pixels.
• Figure 5: Difference Views providing two possibilities for to support the inspection of the pairwise differences in the distributions, absolute and relative.