Keio University Human Biology-Microbiome-Quantum Research Center (WPI-Bio2Q) will hold a seminar as follows.
This is an event for faculty, students, and staff of Keio University.  We sincerely look forward to your participation.

Abstract

Variational Quantum Algorithms (VQAs) and Quantum Machine Learning (QML) models face significant convergence and scaling bottlenecks, primarily driven by the trivial initialization of unentangled product states (|0⟩⊗n) and the widespread emergence of barren plateaus. Standard feature maps frequently fail to preserve high-order classical statistical dependencies, compressing complex multi-modal distributions into independent qubit rotations.

In this work, we propose a physics- and data-informed state preparation protocol that utilizes a Double Covariance Model (DCM) to directly synthesize correlated quantum states—represented generally as mixed density operators ρ0(C1, C2)—from empirical classical datasets. By capturing bi-directional covariance structures (modeling both feature-level and sample/temporal-level dependencies C1 and C2), the DCM generates a structured, entangled initial state prior to variational optimization. We demonstrate the utility of this warm-starting scheme in high-dimensional biomedical applications, specifically multiomics and organ-microbiome network analyses (e.g., cross-modality signaling studied at research centers such as WPI-Bio2Q). Biological datasets naturally exhibit multi-tiered co-dependencies, such as intra-modality microbial strain interactions and cross-modality metabolite-organ signaling. Mapping these twin covariance layers directly onto the quantum register substantially reduces the depth required for trainable ansatz layers (U(θ)) while mitigating vanishing gradient bounds (Var[∂θE] > O(2-n)).

Finally, we evaluate implementation paradigms for deploying DCM-generated density operators on near-term hardware—via ancilla-based purification, stochastic convex sampling, and parameterized dissipative channels—showing that data-driven covariance initialization provides a viable pathway toward demonstrable quantum advantage in complex, non-Gaussian biological learning tasks.