  Quantifying the dynamics of tumor burden reveals useful information about
cancer evolution concerning treatment effects and drug resistance, which play a
crucial role in advancing model-informed drug developments (MIDD) towards
personalized medicine and precision oncology. The emergence of Quantum Machine
Intelligence offers unparalleled insights into tumor dynamics via a quantum
mechanics perspective. This paper introduces a novel hybrid quantum-classical
neural architecture named $\eta-$Net that enables quantifying quantum dynamics
of tumor burden concerning treatment effects. We evaluate our proposed neural
solution on two major use cases, including cohort-specific and patient-specific
modeling. In silico numerical results show a high capacity and expressivity of
$\eta-$Net to the quantified biological problem. Moreover, the close connection
to representation learning - the foundation for successes of modern AI, enables
efficient transferability of empirical knowledge from relevant cohorts to
targeted patients. Finally, we leverage Bayesian optimization to quantify the
epistemic uncertainty of model predictions, paving the way for $\eta-$Net
towards reliable AI in decision-making for clinical usages.
