On the status of current quantum machine learning software

• URL: http://arxiv.org/abs/2503.08962v1
• Date: Tue, 11 Mar 2025 23:55:10 GMT
• Title: On the status of current quantum machine learning software
• Authors: Manish K. Gupta, Tomasz Rybotycki, Piotr Gawron,
• Abstract summary: We investigate how difficult it is to run a hybrid quantum-classical model on a real, publicly available quantum device.<n>We also analyzed the costs of such endeavor and the change in quality of model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent advancements in noisy intermediate-scale quantum (NISQ) devices implementation allow us to study their application to real-life computational problems. However, hardware challenges are not the only ones that hinder our quantum computation capabilities. Software limitations are the other, less explored side of this medal. Using satellite image segmentation as a task example, we investigated how difficult it is to run a hybrid quantum-classical model on a real, publicly available quantum device. We also analyzed the costs of such endeavor and the change in quality of model.

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