Signal Intelligence API

Quantum Signal Characterisation

First-principles signal analysis and domain-aware hardware diagnostics without training data, protocol libraries, or client-side GPUs. Send a time-series to /api/characterise, or use /api/v1/diagnose for hardware-aware routes including NV diamond, Jaynes-Cummings, topological screening, correlated loss, QEC syndrome, and qubit degradation.

Sparseical routes add sparse-state evidence, hardware capture validation, and repo-side compute artefacts with strict claim boundaries.

Production API base URL
https://signal.sparse-supernova.com

Prepend this host to every path below (e.g. /api/characterise, /api/v1/diagnose). Send header X-API-Key on authenticated routes.

Sparse Signal GPT

Custom ChatGPT with Actions wired to this API — run live phenomenon fixtures, hardware diagnose routes, and qubit polling without uploading large trace files.

Open in ChatGPT →
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": [
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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

FieldDescription
characterisation.classificationArray of tags: STRUCTURED, ANTI_CORRELATED, MODULATED, etc.
characterisation.temporal_profileCORRELATED, ANTI_CORRELATED, or NEUTRAL
characterisation.anomaly_levelHIGH, MODERATE, or LOW
characterisation.confidence0.0 – 1.0 classification confidence
phenomena_detectedTriggered Live / v1.0 detectors only (see status table below); each entry has detector and confidence
metricsRaw/anomalous event counts, autocorrelation lags, burst ratio
processingThreshold 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

DomainUse caseVerdicts
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.

FieldValue
hardware_domainnv_diamond
Trace length500-point FID
config.prefilterma11
EncodingSingle FID amplitude trace
Avoid2000pt raw/no-ma11 as contamination FLAG route
AvoidRich 5-feature encoding for this branch
MetricResult
PASS / FLAG / INDETERMINATE80 / 17 / 3
Pure ¹²C PASS60/60
Burst decoy PASS20/20
Contaminated FLAG14/15
Contaminated false PASS0
Overall synthetic sweep accuracy99/100
Estimated DEER follow-up reduction82.1%

Topological screening — 5-feature encoding (`topo_shim_v0`)

Raw conductance does not work. Each bias point must be encoded as 5 features (G, V, B, dG/dV, Gzbp) and flattened into trace[]. Minimum M = 26 bias points (trace ≥ 130). Recommended M = 100 (trace = 500). See topo_shim_v0.mjs and API_MASTER.md.

FieldValue
hardware_domaintopological_screening
Trace lengthM × 5 (e.g. 500 for M = 100)
Encodingtopo_shim_v0 — client-side shim, no API change
LogicInverted: LOW events + LOW anomaly = stable ZBP (PASS)
AvoidRaw 1D conductance without encodeSweep()

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",
  "times": [
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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"
  }
}
MetricResult
Probe 05 statusPASS
Cases completed180 / 180
Stable skip rate100%
Stable false positive rate0%
Mild 1000-shot FLAG rate100%
Medium 400-shot FLAG rate100%
Heavy 400-shot FLAG rate100%
Overall degradation FLAG rate91.9%
Estimated routing-unit saving19.3%

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.

MethodPathBehaviour
GET/api/v1/diagnose/fixture/qec_chiplet_test3Canned verified FLAG_LEAKAGE (no body)
POST/api/v1/diagnose/fixtureBody {"name":"qec_chiplet_test3"} — live 9×9×900 run
GET/api/v1/diagnose/fixturesFixture 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
RegimeRoute
Nonpersistent random syndrome noisePASS → SKIP_DENSE_DECODE
High-density nonpersistent burst decoyPASS → SKIP_DENSE_DECODE
Clustered persistent leakage islandFLAG_LEAKAGE → RUN_DENSE_DECODE_AND_ESCALATE
Repeated single-site persistence across PASS windowsRETRY_SHORT_WINDOW
Any FLAG_LEAKAGE in a 3-window groupRUN_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 (NV diamond)

Illustrative only — not the response from the runnable NV payload above. The live NV diagnose example (500-point trace, prefilter: ma11) typically returns FLAG on production. Shape below matches a real FLAG response; your metrics may differ.

Illustrative only { "domain": "nv_diamond", "domain_diagnostic": { "verdict": "FLAG", "confidence": "high", "interpretation": "Event count at embedding floor with zero gating structure. Consistent with multi-frequency Ising beating from ¹³C nuclear bath.", "action": "Crystal flagged for additional characterisation. Consider DEER spectroscopy or secondary FID at different orientation.", "rule_applied": "nv_diamond.floor_events_zero_gating" }, "prefilter_applied": "ma11", "caveats": [ "NV diamond screening validated on simulated QuTiP Lindblad traces — not real hardware FID.", "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

Assumes samples is an array of at least 128 finite numbers (build or load your trace before calling).

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

Assumes samples is an array of at least 128 finite numbers.

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

Requires local rtl_sdr hardware and a helper such as iq_to_json.py to produce a samples array (≥128 points). Not runnable in-browser.

# 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

Assumes trace is an array of at least 128 finite numbers in [−1, 1] for nv_diamond.

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

Assumes trace is an array of at least 128 finite numbers.

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

Forbidden claim: This proves a new physical particle.

IBM Kingston June 2026

Quantum sparse compute (hardware validated)

Three experiments on ibm_kingston (127-qubit heavy-hex, Open Plan) establish that sparse signal structure reduces quantum work for anomaly detection. Full write-up: API_MASTER.md Appendix G.

ExperimentJob IDCore result
Sparse QEC syndrome gatingd8ftgjg7jphs739mqc2096.4% null syndromes — sparse gate skips classical decode on 987/1024 shots
SS amplitude fidelityd8fu33o7jphs739mr3l0Quantum cosine = classical SS cosine (error 0.000638); TVD 0.48 sparse vs dense on hardware
SWAP test shot efficiencyd8fuccjo3njc73f0seggSparse anomaly: 21 shots @ 95%; dense: 31 shots — 1.48× fewer executions for sparse

Scripts: sparse_quantum_ibm.py, ss_quantum_v2.py, ss_quantum_v3.py. The SWAP test uses identical circuits for sparse and dense; savings are in shots (circuit executions), not gate count. Safe claim: sparse anomalies are more distinguishable in amplitude space and need fewer IBM runs to confirm at 95% confidence.

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.

MethodPath
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

Majorana 2 / tetron governor (handoff v0): majorana2_governor.mjs (15 unit smokes — npm run test:governor), majorana2_prescreener.mjs (10 unit smokes — npm run test:prescreener), extract_data.py (Zenodo H5 → JSON). Parity triage uses fitted τZ directly (no API); topo/CQ use live diagnose + hardware upload. Do not post raw zmpr IQ to qubit_degradation. CQ capture: telegraph_mode: true, device: tetron_cq. Docs: API_MASTER.md Appendix F, docs/MAJORANA2_HANDOFF_V0.md, results MAJORANA2_PRESCREENER_V0_RESULTS.md (13,439 points, 2,818 flagged). IBM Kingston sparse-compute: Appendix G.

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