Hybrid Classical-Quantum architecture for vectorised image classification of hand-written sketches

TL;DR

This paper explores a hybrid classical-quantum approach for vectorized sketch recognition, demonstrating promising results with low-complexity models on a new data representation, addressing current hardware limitations.

Contribution

It introduces a vector-based representation for sketches and evaluates hybrid classical-quantum models on this data, a novel approach in the context of image classification.

Findings

Hybrid models show competitive performance on sketch recognition.

Vector representation simplifies benchmarking quantum models.

Results are promising given current hardware constraints.

Abstract

Quantum machine learning (QML) investigates how quantum phenomena can be exploited in order to learn data in an alternative way, \textit{e.g.} by means of a quantum computer. While recent results evidence that QML models can potentially surpass their classical counterparts' performance in specific tasks, quantum technology hardware is still unready to reach quantum advantage in tasks of significant relevance to the broad scope of the computer science community. Recent advances indicate that hybrid classical-quantum models can readily attain competitive performances at low architecture complexities. Such investigations are often carried out for image-processing tasks, and are notably constrained to modelling \textit{raster images}, represented as a grid of two-dimensional pixels. Here, we introduce vector-based representation of sketch drawings as a test-bed for QML models. Such a lower-dimensional data structure results handful to benchmark model's performance, particularly in current transition times, where classical simulations of quantum circuits are naturally limited in the number of qubits, and quantum hardware is not readily available to perform large-scale experiments. We report some encouraging results for primitive hybrid classical-quantum architectures, in a canonical sketch recognition problem.