Resource-efficient quantum subspace diagonalization algorithms can improve the clustering and manifold discovery in single-cell transcriptomics of autoimmune diseases such as Multiple Sclerosis.
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
- Machine Learning for analysis of Multiple Sclerosis cross-tissue bulk and single-cell transcriptomics data
Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system whose molecular mechanisms remain incompletely understood. In this study, we developed an end-to-end machine learn...
- Resource-efficient Quantum Algorithms for Selected Hamiltonian Subspace Diagonalization
Quantum algorithms for selecting a subspace of Hamiltonians to diagonalize have emerged as a promising alternative to variational algorithms in the NISQ era. So far, such algorithms, which include the...
- HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts
Single-cell perturbation studies face dual heterogeneity bottlenecks: (i) semantic heterogeneity--identical biological concepts encoded under incompatible metadata schemas across datasets; and (ii) st...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.