Quantum Pattern Matching for Early Prediction of Blood Culture Positivity: A Theoretical Biomedical Framework

Rapid identification of bloodstream infections is critical for reducing sepsis-related mortality, as each hour of delay in antibiotic administration increases patient risk. Current automated blood culture systems depend on threshold-based $\text{CO}_2$ monitoring, typically requiring 12–18 hours before declaring positivity. While subtle kinetic fluctuations often emerge within the first 1–4 hours, distinguishing these early signals from noise requires rigorous comparison against vast datasets of known growth signatures. In this biomedical engineering proposal, we introduce a quantum-enhanced framework to accelerate this early identification process. Instead of simple thresholding, the problem is modeled as a nearest-neighbor search against a massive library of $N$ reference kinetic patterns (representing various pathogens and growth conditions). We propose a hybrid pipeline where patient sensor data is compared against a quantum superposition of these reference patterns. By utilizing Grover’s amplitude amplification , the system can identify the optimal matching pattern in $O(\sqrt{N})$ iterations, offering a quadratic speed-up over the classical $O(N)$ linear search required for exhaustive database matching. This theoretical approach addresses the computational bottleneck of real-time diagnostics against large-scale libraries, potentially enabling a reproducible “early-warning” layer hours before standard positivity. This study reports multi-seed simulation averages with $95\%$ confidence intervals to validate the search mechanism and outlines a roadmap for integrating quantum algorithms into clinical microbiology workflows.