On the status of current quantum machine learning software
This work addresses software challenges in quantum machine learning for researchers and practitioners, but it is incremental as it builds on existing NISQ device studies.
The study examined the difficulty and costs of running a hybrid quantum-classical model on a publicly available quantum device, using satellite image segmentation as an example, and found that software limitations significantly hinder quantum computation capabilities.
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.