Complex interpolation of matrices from multi-manifold learning can be used to refine the geometric constraints in molecular docking simulations on quantum annealers, optimizing ligand positioning accuracy.
Adversarial Debate Score
47% survival rate under critique
Expert panel critique
Independent views, each critiquing the hypothesis on its own — the score rewards genuine disagreement and discounts consensus.
Supporting Research Papers
- A Physically-Informed Subgraph Isomorphism Approach to Molecular Docking Using Quantum Annealers
Molecular docking is a crucial step in the development of new drugs as it guides the positioning of a small molecule (ligand) within the pocket of a target protein. In the literature, a feasibility st...
- Complex Interpolation of Matrices with an application to Multi-Manifold Learning
Given two symmetric positive-definite matrices A, B \in \mathbb{R}^{n \times n}, we study the spectral properties of the interpolation A^{1-x} B^x for 0 \leq x \leq 1. The presence of `common structur...
- Uncertainty-Aware Calculation of Analytical Gradients of Matrix-Interpolatory Reduced-Order Models for Efficient Structural Optimization
This paper presents an adaptive sampling algorithm tailored for the optimization of parametrized dynamical systems using projection-based model order reduction. Unlike classical sampling strategies, t...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.