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 quantum 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 phenomena detectors surface anomalous signatures.

The pipeline runs server-side. You receive only the characterisation — classification tags, temporal profile, anomaly level, and detected phenomena.

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.

Note for Python clients: Cloudflare may block requests that use the default User-Agent: Python-urllib/3.x. Set a reasonable User-Agent (e.g. QuantumSignalClient/1.0 or YourApp/1.0) in your headers to avoid blocks.

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`	Array of triggered mathematical detectors with 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 12 mathematical signatures

What We Detect

The pipeline tests for twelve mathematical phenomena in every signal. Each detector asks a specific structural question — not "is this a known protocol?" but "is this signal doing something mathematically unusual?"

Physical Phenomena

Quantum Zeno: Measurement frequency correlates with delayed transitions
Criticality: Self-organised criticality in event magnitude distribution
Information Backflow: Non-Markovian temporal correlations
Fractal Dimension: Chaotic structure in state-space trajectories
Topological Transitions: Phase changes in signal geometry
Penrose OR: Gravity-scale collapse events

Structural Signatures

Weak Values: Anomalous measurement outcomes outside expected bounds
Quantum Darwinism: Environmental redundancy and information copying
Retrocausality: Future events correlating with past states
Teleportation Shadow: Cross-channel correlations without direct coupling
Simulation Glitches: Quantisation artifacts and periodic patterns
Consciousness Correlation: Observer-dependent measurement bias

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", "User-Agent": "QuantumSignalClient/1.0", }, json={"samples": samples} ) result = resp.json() print(result["characterisation"]["classification"])

User-Agent: Cloudflare blocks the default Python-urllib/3.x; include any reasonable User-Agent (e.g. above) to avoid blocks.

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