Quantum-Inspired Signal Extraction for Biomarker Discovery & Antibiotic Resistance

Antibiotic resistance and biomarker discovery share a common computational challenge:
biologically meaningful signals are sparse, high-dimensional, and obscured by evolutionary noise.

While large genomic and phenotypic datasets now exist across clinical, environmental, and experimental settings, classical analytic methods struggle to identify the early signals that precede resistance or define actionable biomarkers. Detection remains largely reactive after resistance has already emerged or phenotypes have stabilized.

Optimizing Patient Stratification: Minimizing Discrepancies Between Patient Groups in Clinical Trials

APPROACH

This work applies quantum-inspired optimization and probabilistic modeling to extract compact, interpretable signal sets from complex biological data without requiring quantum hardware.

The framework combines:

  • Optimization-based feature selection to reduce thousands of genomic variables to small, non-redundant panels

  • Probabilistic and causal modeling to preserve mechanistic structure

  • Longitudinal analysis to capture early evolutionary trajectories rather than end-stage outcomes

The result is a signal representation designed for early detection, interpretability, and downstream decision-making.

WHAT WAS DEMONSTRATED

  • Compression of >5,000 genomic and transcriptomic features into compact biomarker and mutation sets

  • Identification of early genomic signals associated with resistance-prone evolutionary trajectories

  • Improved separation of causal structure from correlation in high-noise regimes

This work is supported by external funding and experimental validation, combining quantum-inspired computation with microbial evolution assays and large-scale resistance datasets.

NIH CHALLENGE WINNERS

This work was recognized as a winning project in the NIH Quantum Computing Challenge, a national initiative evaluating practical, high-impact applications of quantum and quantum-inspired methods in biomedical research.

The award validates the scientific rigor and translational relevance of applying quantum-inspired signal extraction to complex biological problems, specifically in regimes where classical approaches struggle with scale, noise, and interpretability.

Recognition through this program reflects independent expert assessment, not theoretical promise, and supports the feasibility of deploying these methods in real-world biomedical and public-health contexts.