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

All paths are relative to https://signal.sparse-supernova.com

POST /api/characterise Analyse time-series data

                POST
                /api/v1/diagnose
                Domain-aware diagnostic wrapper
            

                GET
                /api/v1/diagnose/fixture/qec_chiplet_test3
                Canned QEC fixture — FLAG_LEAKAGE (no payload)
            

POST /api/v1/diagnose/fixture Run stored diagnose fixture live ({"name":"..."})

GET /api/v1/diagnose/fixtures List diagnose fixture names

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

GET /api/health Service health check

GET /api Endpoint catalogue

GET /api/v1/sparseical Sparseical Candidate v0 catalogue

GET /api/v1/sparseical/status Sparseical deployment status

GET /api/v1/sparseical/evidence-pack Evidence pack summary

POST /api/v1/sparseical/canonical Canonical Sparseical packet

POST /api/v1/sparseical/gatekeeper Gatekeeper check

POST /api/v1/sparseical/cascade Half-adder cascade

POST /api/v1/sparseical/qec-carry QEC carry check

GET /api/v1/sparseical/hardware/status Hardware capture readiness

POST /api/v1/sparseical/hardware/fixture Built-in hardware capture fixture

POST /api/v1/sparseical/hardware/upload Upload CSV/JSON/NDJSON captures

POST /api/v1/sparseical/hardware/capture Validate pre-parsed arrays

Sparseical PC Compute Runtime artefacts are repo-side. They do not add live Worker execution routes in API v1.0.

Request Format

Runnable request — valid JSON below with 128 finite samples. Copy or use Load characterise example in Try It.

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

{
  "samples": [
    0,
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  ],
  "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

Response sample for documentation — not a request payload.

                Illustrative only
{
  "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
  }
}

Domain Diagnose v1 Structural + fit/episode branches

POST /api/v1/diagnose

Structural domains use /api/characterise internally plus static domain rules. Two domains bypass the embedding for their primary verdict: qubit_degradation uses weighted decay fitting, and qec_syndrome uses direct episode detection for persistent leakage islands.

Advisory only. PASS means no evidence of the flagged condition in that domain’s rules — not proof of purity, topology, or non-Markovianity. Every response includes caveats describing validation basis and limits.

Request shapes differ by domain. Do not treat every diagnose domain as trace[] input. qubit_degradation and qec_syndrome have different request bodies.

Request Format

Runnable copy builds a 500-point NV trace in the browser (≥128 required). Use fixture GET for QEC without building syndrome frames.

Click Copy runnable JSON or Load NV diagnose example (500-point trace).

hardware_domain (required) — nv_diamond, jaynes_cummings, topological_screening, qubit_degradation, correlated_loss, qec_syndrome, generic.

trace is required for structural domains only. qubit_degradation uses times, baseline_trace, current_trace, and shots_per_point. qec_syndrome uses syndrome_frames, grid_height, grid_width, and optional rounds / config.window_size. Live minimum: 512 rounds for qec_syndrome (fewer frames return a validation error).

config.prefilter — Optional "ma11" for 11-point moving average before analysis; default "none".

metadata — Optional; echoed in the response for traceability.

Domain Verdicts

Domain	Use case	Verdicts
`nv_diamond`	NV centre FID / ¹³C spin-bath pre-screen	PASS FLAG INDETERMINATE
`jaynes_cummings`	Open-system revival vs Markovian baseline	PASS FLAG INDETERMINATE
`topological_screening`	Rich-encoded sweep; inverted logic (LOW events + LOW anomaly = stable)	PASS FLAG INDETERMINATE
`correlated_loss`	Neutral atom / trapped-ion arrays — organised vs random loss	PASS FLAG INDETERMINATE
`qubit_degradation`	FID weighted decay-fit — baseline vs current (not `trace`)	STABLE FLAG_DEGRADATION INDETERMINATE
`qec_syndrome`	Persistent QEC leakage islands (not raw density)	PASS FLAG_LEAKAGE INDETERMINATE
`generic`	Raw characterisation only	INFO

NV diamond — confirmed input contract

NV PASS is advisory. It is not diamond certification and not a replacement for DEER spectroscopy. Current validation basis is simulated QuTiP Lindblad traces, not real hardware FID.

Field	Value
`hardware_domain`	`nv_diamond`
Trace length	500-point FID
`config.prefilter`	`ma11`
Encoding	Single FID amplitude trace
Avoid	2000pt raw/no-ma11 as contamination FLAG route
Avoid	Rich 5-feature encoding for this branch

Metric	Result
PASS / FLAG / INDETERMINATE	80 / 17 / 3
Pure ¹²C PASS	60/60
Burst decoy PASS	20/20
Contaminated FLAG	14/15
Contaminated false PASS	0
Overall synthetic sweep accuracy	99/100
Estimated DEER follow-up reduction	82.1%

Qubit degradation — weighted decay-fit branch

The qubit_degradation route is not an embedding event-count branch. It fits paired baseline/current FID traces and detects statistically significant T₂/T₂* degradation. Qubit degradation is WORKING under the validated FID/Gaussian T₂* input contract.

Runnable request — 128 paired time points (API minimum for arrays). Copy or use Diagnose: Qubit Degradation in Try It.

POST /api/v1/diagnose
X-API-Key: demo-sparse-supernova-2026
Content-Type: application/json

{
  "hardware_domain": "qubit_degradation",
  "experiment": "fid",
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  "baseline_trace": [
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  "shots_per_point": 1000,
  "metadata": {
    "workflow": "qubit_degradation_energy_routing",
    "fixture_policy": "paired baseline/current traces with shared noise draw"
  }
}

Metric	Result
Probe 05 status	PASS
Cases completed	180 / 180
Stable skip rate	100%
Stable false positive rate	0%
Mild 1000-shot FLAG rate	100%
Medium 400-shot FLAG rate	100%
Heavy 400-shot FLAG rate	100%
Overall degradation FLAG rate	91.9%
Estimated routing-unit saving	19.3%

Works under the validated FID/Gaussian T₂* input contract.
Synthetic baseline/current traces should share the same noise draw; only decay parameter should change.
Does not reduce cryostat base load directly.
Energy saving is routing-unit estimate, not direct electricity-meter measurement.

QEC syndrome — persistent leakage-island detection

The qec_syndrome branch detects spatiotemporal persistence, not raw syndrome density. High-density random syndrome noise may PASS if defects are nonpersistent. Production requires ≥512 rounds of binary syndrome frames (values 0 or 1). Use 600–900 rounds for chiplet-style demos; the 2-round shape below is illustrative only and will be rejected by the API.

                Shape only (not valid API payload)
{
  "hardware_domain": "qec_syndrome",
  "syndrome_frames": [[0,0,1,0],[0,0,1,0]],
  "grid_height": 2,
  "grid_width": 2,
  "rounds": 2,
  "config": { "window_size": 25 }
}
// NOT VALID — API requires ≥512 rounds. Use Fixture: QEC chiplet (GET) in Try It.

Diagnose fixtures (GPT / integrations)

When clients cannot upload large syndrome_frames arrays (e.g. ChatGPT Actions), use server-side fixtures. Claim level: api_level_structural_evidence_only.

Method	Path	Behaviour
GET	`/api/v1/diagnose/fixture/qec_chiplet_test3`	Canned verified `FLAG_LEAKAGE` (no body)
POST	`/api/v1/diagnose/fixture`	Body `{"name":"qec_chiplet_test3"}` — live 9×9×900 run
GET	`/api/v1/diagnose/fixtures`	Fixture catalogue

# GPT one-liner (canned) curl -s -H "X-API-Key: demo-sparse-supernova-2026" \ https://signal.sparse-supernova.com/api/v1/diagnose/fixture/qec_chiplet_test3

Regime	Route
Nonpersistent random syndrome noise	PASS → SKIP_DENSE_DECODE
High-density nonpersistent burst decoy	PASS → SKIP_DENSE_DECODE
Clustered persistent leakage island	FLAG_LEAKAGE → RUN_DENSE_DECODE_AND_ESCALATE
Repeated single-site persistence across PASS windows	RETRY_SHORT_WINDOW
Any FLAG_LEAKAGE in a 3-window group	RUN_DENSE_DECODE_AND_ESCALATE

QEC energy-routing policy (Probe 04 Policy C): 300 scenarios, 900 API calls, 0 API errors; baseline unsafe skip 23.5% → Policy C unsafe skip 0%; false escalation 0%; clustered leakage escalation 100%; retry rate 15.7%; safe policy estimated saving 30.1%. Policy C is client-side aggregation, not a backend model change. Does not claim cryostat/base-power reduction. Energy saving is estimated from routing units.

Example Response

Response sample for documentation — ellipsis is not valid JSON.

                Illustrative only
{
  "timestamp": "2026-05-17T06:53:22Z",
  "domain": "nv_diamond",
  "characterisation": { ... },
  "metrics": { "raw_events": 186, "gating_reduction": "92.5%", ... },
  "domain_diagnostic": {
    "verdict": "PASS",
    "confidence": "high",
    "interpretation": "Smooth monotonic dephasing detected...",
    "action": "Proceed to standard calibration.",
    "rule_applied": "nv_diamond.event_gating_dual_gate",
    "evidence": {
      "primary_metric": "events",
      "primary_value": 186,
      "secondary_metric": "gating_pct",
      "secondary_value": 92.5,
      "ac1": 0.946
    }
  },
  "prefilter_applied": "none",
  "caveats": [
    "NV diamond screening validated on 90 QuTiP Lindblad traces...",
    "Domain interpretation is advisory — not a substitute for DEER spectroscopy."
  ]
}

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

Try It Now

Test /api/characterise or /api/v1/diagnose. Paste JSON with samples, or domain-specific diagnose bodies (not all domains use trace).

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 cross-observer correlation experiments. Detector names are physics-inspired heuristic labels, not laboratory proofs. 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	Cross-observer correlation	Observer-dependent measurement bias	—	Roadmap

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

Integration

Quick Start

Characterise

cURL

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

Domain diagnose

cURL

Python

resp = requests.post( "https://signal.sparse-supernova.com/api/v1/diagnose", headers={"X-API-Key": "demo-sparse-supernova-2026"}, json={ "hardware_domain": "nv_diamond", "trace": trace, "config": {"prefilter": "ma11"}, }, ) d = resp.json() print(d["domain_diagnostic"]["verdict"], d["domain_diagnostic"]["interpretation"])

JavaScript / Node

                        
const resp = await fetch(
  "https://signal.sparse-supernova.com/api/v1/diagnose",
  {
    method: "POST",
    headers: {
      "X-API-Key": "demo-sparse-supernova-2026",
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      hardware_domain: "jaynes_cummings",
      trace,
      config: { prefilter: "none" },
    }),
  }
);
const { domain_diagnostic, caveats } = await resp.json();
console.log(domain_diagnostic.verdict, domain_diagnostic.action);
console.log(caveats);

QEC diagnose fixture (no syndrome payload)

cURL — canned GET

curl -s -H "X-API-Key: demo-sparse-supernova-2026" \ https://signal.sparse-supernova.com/api/v1/diagnose/fixture/qec_chiplet_test3

cURL — live POST

curl -s -X POST \ -H "X-API-Key: demo-sparse-supernova-2026" \ -H "Content-Type: application/json" \ -d '{"name":"qec_chiplet_test3"}' \ https://signal.sparse-supernova.com/api/v1/diagnose/fixture

Limits

What this does not do

Does not run a quantum computer.
Does not certify diamond purity.
Does not replace DEER spectroscopy.
Does not prove Majorana/topology.
Does not directly measure electricity savings.
Does not reduce cryostat base power directly.
Does not prove a physical particle.
Does not prove hardware quasi-particle compute.
Sparseical PC Compute evidence is repo-side and finite/bounded unless explicitly framed as abstract-machine proof assumptions.

Forbidden claim: This proves a new physical particle.

Sparseical

Sparseical routes

Sparseical Candidate v0 routes expose sparse-state evidence, canonical packets, gatekeeper/cascade checks, QEC carry, evidence pack summaries, and hardware capture validation. Hardware capture PASS is not proof of a physical particle, hardware quasi-particle, bare-metal compute, or Turing completeness.

Method	Path
GET	`/api/v1/sparseical`
GET	`/api/v1/sparseical/status`
GET	`/api/v1/sparseical/evidence-pack`
POST	`/api/v1/sparseical/canonical`
POST	`/api/v1/sparseical/gatekeeper`
POST	`/api/v1/sparseical/cascade`
POST	`/api/v1/sparseical/qec-carry`
GET	`/api/v1/sparseical/hardware/status`
POST	`/api/v1/sparseical/hardware/fixture`
POST	`/api/v1/sparseical/hardware/upload`
POST	`/api/v1/sparseical/hardware/capture`

Probes 11–16 and PC Compute Runtime are repo-side artefacts. They are not live Worker execution routes.

Website / interactive docs: signal.sparse-supernova.com/#api-docs