Quantum Deep Learning Pipeline for Next Generation Network Biology
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Purpose
Module discovery in omics networks is central to interpretation. Classical pipelines capture broad community structure, but exact search for small, connected, topology aware modules is combinatorial, making exhaustive solutions impractical at genome scale. Moreover, most quantum clique formulations to date optimize only maximal density on non biological graphs, which mismatches the heterogeneous shapes of real biological modules.
Methods
We trained a symmetric 10-dimensional autoencoder on GTEx normals (Heart Left Ventricle; Muscle Skeletal; UCSC Xena Toil) to obtain tissue-specific latent representations. For each tissue we built a 10*10 latent-node correlation graph and formulated a QUBO that rewards strong edges and penalizes isolated selections (edge threshold τ=0.80). QAOA (depth=3, COBYLA) generated high-probability bitstrings, which we post-filtered to retain connected, non-overlapping subsets; decoder triggered gene sets and multi-library enrichment provided functional interpretation.
Results
QAOA distilled the Heart graph to three dyads (2-node modules with strong-edge sums ≈0.89/0.95), pairing conduction with mitochondria, conduction with ECM/adhesion, and two contractile nodes. In Muscle, QAOA returned one dominant 6-node module (edge-sum ≈7.0) integrating contractile machinery, electrophysiology, mitochondrial metabolism, and translation; a weaker ECM leaning triplet was visible but fell below the top 10 threshold.
Conclusion
QAOA based quantum optimization yields discrete, testable modules from latent correlations that match tissue programs. Despite a 10-node demo (current limits), the scale-agnostic design extends to 10^3 10^5 nodes and multi omics via hierarchical compression and hybrid search supporting quantum modeling for next generation network biology.
