Quantum Sensing — the data layer over ultra-sensitive sensors

Quantum Sensing is a data and abstraction layer over ultra-sensitive quantum sensors. It is deliberately not the physics — it is the registry, ingest, storage, and analysis that sit above the hardware, so a program can treat very different sensor classes through one consistent interface.

The sensor registry:

Register sensors across the major quantum classes — magnetometry (NV-center and optically-pumped magnetometers), gravimetry (cold-atom), timing (optical and atomic clocks), quantum imaging, and quantum radar or lidar. Each sensor carries a typed CapabilityDescriptor: what it measures, its canonical unit, and its sample-rate class. That descriptor uses the same shape the Fabric uses for MCP tools, so a sensor is described the same way any other capability is.

The provider seam is dormant by default:

No vendor is hardcoded. A sensor does nothing until real hardware and credentials are configured — the provider seam ships dormant on purpose, so the module is honest about what is real versus what is waiting on hardware.

Ingesting readings:

Readings come in as a class-agnostic time series. Spatial readings carry latitude and longitude and overlay the location fabric, so a magnetometer sweep or a gravimetry survey shows up on the map alongside everything else spatial.

Into the Data Lake, no export loop:

Readings project into the Data Lake as a queryable quantumReadings dataset. You query sensor data with the same tools you query the rest of your org's data — there is no separate export step to keep in sync.

The analysis pipeline:

A pipeline does calibration and bias/drift correction, noise filtering, and rolling-baseline anomaly detection. Where the real physics has to plug in, the steps are honest DSP stubs rather than hidden magic — the abstraction is real and the seam for the hardware is clearly marked.