Quantum Sentry
An experimental anomaly-detection project exploring a hybrid classical–quantum pipeline, beginning with flight telemetry and extending toward business data. The pipeline is achievable; the mathematics and evidence needed to establish its usefulness are still developing.
The question
What could we learn from the tiny irregularities that disappear into a much larger dataset?
Flight telemetry was my starting point. I wanted to explore whether a different computational approach could help isolate unusual datapoints worth investigating.
That curiosity expanded into business data, where subtle deviations might have practical value, and a more speculative question about sensing: could richer material data and AI help distinguish surface information from signals associated with what lies beneath it?
The approach
I explored a Python-based pipeline combining conventional processing with quantum computing tools, working through how data could move through the system and produce candidate anomalies.
The business-data application became the most practical direction. The sensing idea remains a much longer research path. It would require suitable measurements, a defensible physical model, and evidence that the information I’m looking for is present in the data at all.
That distinction matters. Software can’t reveal something a sensor never measured just because the idea sounds cool as shit.
Where it stands
The work demonstrated that the proposed technology pipeline could be assembled. More work is needed on the Python mathematics, validation, and other foundational pieces before I can claim reliable anomaly detection.
I haven’t established a quantum advantage or demonstrated the proposed subsurface sensing capability.
What stays with me
This project gives my curiosity room to get ambitious while making me accountable for the details.
I enjoy following an unusual possibility far enough to discover what it actually demands. Sometimes that produces a working component. Sometimes it produces a better question and a much longer reading list. Both move the work forward.