  Quantum machine learning (QML) has the potential for improving the
multi-label classification of rare, albeit critical, diseases in large-scale
chest x-ray (CXR) datasets due to theoretical quantum advantages over classical
machine learning (CML) in sample efficiency and generalizability. While prior
literature has explored QML with CXRs, it has focused on binary classification
tasks with small datasets due to limited access to quantum hardware and
computationally expensive simulations. To that end, we implemented a Jax-based
framework that enables the simulation of medium-sized qubit architectures with
significant improvements in wall-clock time over current software offerings. We
evaluated the performance of our Jax-based framework in terms of efficiency and
performance for hybrid quantum transfer learning for long-tailed classification
across 8, 14, and 19 disease labels using large-scale CXR datasets. The
Jax-based framework resulted in up to a 58% and 95% speed-up compared to
PyTorch and TensorFlow implementations, respectively. However, compared to CML,
QML demonstrated slower convergence and an average AUROC of 0.70, 0.73, and
0.74 for the classification of 8, 14, and 19 CXR disease labels. In comparison,
the CML models had an average AUROC of 0.77, 0.78, and 0.80 respectively. In
conclusion, our work presents an accessible implementation of hybrid quantum
transfer learning for long-tailed CXR classification with a computationally
efficient Jax-based framework.
