# Quantum Image Classification: Experiments on Utility-Scale Quantum Computers Source: [Quantum Image Classification: Experiments on Utility-Scale Quantum Computers]() Collection index: [llms.txt]() Data updated: 2025-10-25T07:36:42.340Z Authors: Hrant Gharibyan, Hovnatan Karapetyan, Tigran Sedrakyan, Pero Subasic, Vincent P. Su, Rudy H. Tanin, Hayk Tepanyan Published: 2025-04-14T18:00:13.000Z ## Abstract We perform image classification on the Honda Scenes Dataset on Quantinuum's H-2 and IBM's Heron chips utilizing up to 72 qubits and thousands of two-qubit gates. For data loading, we extend the hierarchical learning to the task of approximate amplitude encoding and block amplitude encoding for commercially relevant images up to 2 million pixels. Hierarchical learning enables the training of variational circuits with shallow enough resources to fit within the classification pipeline. For comparison, we also study how classifier performance is affected by using piecewise angle encoding. At the end of the VQC, we employ a fully-connected layer between measured qubits and the output classes. Some deployed models are able to achieve above 90\\% accuracy even on test images. In comparing with classical models, we find we are able to achieve close to state of the art accuracy with relatively few parameters. These results constitute the largest quantum experiment for image classification to date. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()