Quantum Signal Characterisation API

Overview API Operational — v1.0.0

What This Does

The API accepts raw time-series data — voltage samples, IQ pairs, ADC readings — and returns a structural characterisation of what the signal is doing. It works on radio signals, sensor streams, financial data, or any continuous measurement.

How It Works

Your data is converted into state-space trajectories. Adaptive thresholding identifies significant state transitions. Unsupervised anomaly gating filters noise. Spike-based coincidence scoring measures structural similarity. Multi-lag autocorrelation captures temporal patterns. Twelve mathematical signature detectors are defined for the product; seven are live in API v1.0 and may appear in phenomena_detected.

The pipeline runs server-side. You receive only the characterisation — classification tags, temporal profile, anomaly level, and any triggered live detectors.

What Makes It Different

No training data: Fully unsupervised. Works on signals never seen before.
Not pattern matching: Characterises mathematical structure, not protocol signatures.
Universal input: Any time-series. RF, vibration, medical, financial, quantum hardware.
Structural output: Not just "anomaly detected" but what kind of anomaly.
Edge-ready: Designed for neuromorphic hardware at milliwatt power.

API Reference Authentication: X-API-Key header

Endpoints

POST /api/characterise Analyse time-series data

GET /api/demo Run on built-in demo signal

GET /api/health Service health check

Request Format

                        
POST /api/characterise
X-API-Key: demo-sparse-supernova-2026
Content-Type: application/json

{
  "samples": [0.12, -0.34, 0.56, ...],
  "config": {
    "sample_rate": 44100,
    "dim": 64,
    "stride": 16,
    "trajectory_count": 3
  }
}

samples — Array of numbers. Raw voltage, amplitude, IQ magnitude, or any continuous measurement. Minimum 128, maximum 5,000,000.

config — Optional. Defaults work for most signals.

Response Fields

Field	Description
`characterisation.classification`	Array of tags: STRUCTURED, ANTI_CORRELATED, MODULATED, etc.
`characterisation.temporal_profile`	CORRELATED, ANTI_CORRELATED, or NEUTRAL
`characterisation.anomaly_level`	HIGH, MODERATE, or LOW
`characterisation.confidence`	0.0 – 1.0 classification confidence
`phenomena_detected`	Triggered Live / v1.0 detectors only (see status table below); each entry has `detector` and `confidence`
`metrics`	Raw/anomalous event counts, autocorrelation lags, burst ratio
`processing`	Threshold method, trajectory count, processing time

Example Response

                
{
  "success": true,
  "characterisation": {
    "classification": ["STRUCTURED", "ACTIVE", "MODULATED", "HIGH_ANOMALY"],
    "temporal_profile": "CORRELATED",
    "activity_level": "HIGH",
    "anomaly_level": "HIGH",
    "confidence": 0.82
  },
  "phenomena_detected": [
    { "detector": "information_backflow", "confidence": 0.71 },
    { "detector": "quantum_darwinism", "confidence": 0.58 },
    { "detector": "criticality", "confidence": 0.63 }
  ],
  "metrics": {
    "raw_events": 1728,
    "anomalous_events": 106,
    "gating_reduction": "93.9%",
    "autocorrelation_lag1": 0.089,
    "autocorrelation_lag2": -0.041,
    "autocorrelation_lag3": 0.032,
    "burst_ratio": 0.496
  },
  "processing": {
    "threshold": 0.3179,
    "threshold_method": "MAD",
    "processing_time_ms": 230
  }
}

Live Testing Demo key: demo-sparse-supernova-2026

Try It Now

Run the analysis on the built-in demo signal, or paste your own samples.

Ready

Detectors 7 live · 5 roadmap (12 total)

What We Detect

The product defines twelve mathematical signatures. Each asks a structural question — not “is this a known protocol?” but “is this signal doing something mathematically unusual?” Seven are live in API v1.0; five are on the roadmap (product direction, not yet returned in phenomena_detected).

What this is not. It does not run a quantum computer, GR collapse models, or consciousness experiments. Detectors are heuristic flags on jump events and autocorrelation statistics in a classical time-series pipeline. Names are physics-inspired; confidence is an ordinal score, not a calibrated laboratory probability.

#	Category	Signature	Structural question	API `detector`	Status
1	Physical	Quantum Zeno	Measurement frequency correlates with delayed transitions	`quantum_zeno`	Live / v1.0
2	Physical	Criticality	Self-organised criticality in event magnitude distribution	`criticality`	Live / v1.0
3	Physical	Information Backflow	Non-Markovian temporal correlations	`information_backflow`	Live / v1.0
4	Physical	Fractal Dimension	Chaotic structure in state-space trajectories	—	Roadmap
5	Physical	Topological Transitions	Phase changes in signal geometry	—	Roadmap
6	Physical	Penrose OR	Gravity-scale collapse events	`penrose_or`	Live / v1.0
7	Structural	Weak Values	Anomalous measurement outcomes outside expected bounds	—	Roadmap
8	Structural	Quantum Darwinism	Environmental redundancy and information copying	`quantum_darwinism`	Live / v1.0
9	Structural	Retrocausality	Future events correlating with past states	—	Roadmap
10	Structural	Teleportation Shadow	Cross-channel correlations without direct coupling	`teleportation_shadow`	Live / v1.0
11	Structural	Simulation Glitches	Quantisation artifacts and periodic patterns	`simulation_glitches`	Live / v1.0
12	Structural	Consciousness Correlation	Observer-dependent measurement bias	—	Roadmap

Calculation notes (thresholds, heuristics, pipeline): PHENOMENA_CALCULATIONS.md in the API source repository.

Integration

Quick Start

cURL

curl -X POST https://signal.sparse-supernova.com/api/characterise \ -H "X-API-Key: demo-sparse-supernova-2026" \ -H "Content-Type: application/json" \ -d '{"samples": [0.1, -0.3, 0.5, ...]}'

Python

import requests resp = requests.post( "https://signal.sparse-supernova.com/api/characterise", headers={"X-API-Key": "demo-sparse-supernova-2026"}, json={"samples": samples} ) result = resp.json() print(result["characterisation"]["classification"])

JavaScript / Node

                        
const resp = await fetch(
  "https://signal.sparse-supernova.com/api/characterise",
  {
    method: "POST",
    headers: {
      "X-API-Key": "demo-sparse-supernova-2026",
      "Content-Type": "application/json",
    },
    body: JSON.stringify({ samples })
  }
);
const result = await resp.json();
console.log(result.characterisation.classification);

SDR Integration

# Capture IQ from RTL-SDR, pipe to API rtl_sdr -f 462.5625e6 -s 2.4e6 -n 48000 - | \ python3 iq_to_json.py | \ curl -X POST \ -H "X-API-Key: YOUR_KEY" \ -H "Content-Type: application/json" \ -d @- \ https://signal.sparse-supernova.com/api/characterise