qontinuum 0.5.0

The Quantum Engineering Platform: statistical testing, cost and execution intelligence, provider-agnostic plugins, spend guards, and observability for quantum programs

Qontinuum

The Quantum Engineering Platform — statistical testing, cost & execution intelligence, provider-agnostic plugins, and observability for quantum programs.

🌐 Website · 📚 Docs · 📊 Live demo dashboard · 📖 Explainer

Quantum programs don't return values — they return probability distributions sampled from noisy hardware. assert result == expected doesn't work, and nothing in the classical CI toolchain knows that. Qontinuum does.

• qont test — a pytest-style runner for quantum circuits with statistical assertions: total variation distance with shots-aware soundness floors, chi-squared tests, Hellinger fidelity, and snapshot baselines committed as JSON.
• qont cost — estimates what your suite would cost on real hardware across providers (IonQ, Rigetti, IQM, AQT via Braket; IBM; Azure Quantum) before you spend a cent.
• qont route — recommends hardware for your workload: estimated success probability × price, ranked by --optimize cost|fidelity|value.
• qont recommend / plan / health — execution intelligence: rank devices under six strategies with a plain-English explanation of every choice, produce a concrete execution plan with fallbacks, and score per-device reliability from calibration + your run history.
• qont run --on ibm:… --max-cost 5 — executes the suite on real hardware behind an all-or-nothing spend guard (default budget: $0 — it refuses until you authorize).
• qont ci — one command for CI: run the suite, write a markdown report.
• qont dashboard — renders run history as a self-contained HTML observability dashboard (status timeline, per-check statistic trends vs thresholds).
• qont diff / qont hash — semantic, register-name-agnostic circuit diffing and content addressing.
• GitHub Action — posts a sticky PR comment with regression results and the cost table.
• Noise-aware testing — simulate against real IBM device calibration data, offline, for free.
• Plugin ecosystem — providers, simulator backends, and SDKs are plugins; Qiskit, QASM 2/3, Cirq, PennyLane, IBM, and Braket ship built in, and you can add your own via entry points without forking.

…and that's just the core. v0.5 (public beta) is the Quantum Engineering Platform — 124 commands translating the DevOps stack to quantum:

qont lint	ruff for quantum tests — 9 rules incl. the statistical soundness floor (Q001)
qont noise headroom	load-testing for quantum — how much calibration drift survives your test?
qont history bisect	git-bisect for physics — which run first crossed the threshold?
qont budget	QPU FinOps — monthly/total caps enforced before submission
qont test --cached	build caching for simulation — content-addressed seeded results
qont fuzz	flakiness hunting — empirical pass rates with Wilson intervals
qont pack	reproducibility bundles — verify re-runs and two-sample-compares
qont shots plan	shot-count audits — the napkin statistics, mechanized
qont report junit	quantum checks rendered natively in Jenkins/GitLab/Buildkite
qont explain	failure forensics: which outcomes drove the statistic, and why
qont recommend / plan / health	execution intelligence — reason about where to run: fidelity, cost, speed, reliability, with explanations
qont report engineering	one engineering report — recommendation, execution plan (risk + confidence), provider & cost analysis, history
qont telemetry	opt-in Quantum Intelligence Network — anonymous, privacy-first community intelligence, off by default
qont plugin	provider-, backend-, and SDK-agnostic — add hardware or SDKs via plugins, no fork
+ circuit device registry stats bench watch config env doctor init mock schedule …	the full plumbing

Install

pip install qontinuum          # core
pip install 'qontinuum[ibm]'   # + IBM calibration noise models

Quick start

Write a quantum test in any file named q_test_*.py:

from qiskit import QuantumCircuit
from qontinuum import qtest, assert_distribution

@qtest(shots=4000)
def bell_pair():
    qc = QuantumCircuit(2)
    qc.h(0)
    qc.cx(0, 1)
    qc.measure_all()
    return qc

@bell_pair.check
def is_maximally_entangled(result):
    assert_distribution(result, {"00": 0.5, "11": 0.5}, tvd_threshold=0.05)

$ qont test
✓ q_test_bell.py::bell_pair  aer, 4000 shots, 30 ms
    ✓ is_maximally_entangled

1 passed, 0 failed, 0 errors

Statistics done right

Sampling noise means even a perfect circuit never reproduces a distribution exactly. Every assertion is shots-aware: if your threshold is below what the shot count can statistically resolve, Qontinuum refuses to run a test that would be flaky by construction — and tells you the shot count that would fix it:

! is_maximally_entangled  tvd_threshold=0.01 is below the sampling noise floor 0.0289
  at 4000 shots: even a perfect result would fail ~1% of the time.
  Use at least 33380 shots or raise the threshold to >= 0.0289.

Test against real hardware noise — offline and free

@qtest(shots=4000, backend="ibm:manila")   # bundled real calibration snapshot
def bell_under_noise(): ...

@qtest(shots=4000, backend="ibm:brisbane@live")  # today's live calibration (free IBM account)
def bell_today(): ...

Snapshot testing for quantum

@qtest(shots=4000, snapshot=True)
def ghz_5(): ...

qont snapshot update records the distribution as a golden baseline (keyed by the circuit's content hash). Later runs are compared with a two-sample homogeneity test — if the circuit changes, the snapshot is flagged stale for review, exactly like Jest.

Know the cost before you run on hardware

$ qont cost
             Estimated hardware cost — 3 test(s), 12000 total shots
┃ Provider      ┃ Device                ┃ Est. cost ┃
│ AWS Braket    │ Rigetti Cepheus       │     $6.00 │
│ IBM Quantum   │ Heron (Pay-As-You-Go) │     $9.61 │
│ AWS Braket    │ IQM Garnet            │    $18.30 │
│ Azure Quantum │ IonQ Aria 1           │    $63.29 │
│ AWS Braket    │ IonQ Forte            │   $960.90 │
│ Azure Quantum │ Quantinuum H2         │ 137.4 HQC │

Same suite, $6 or $961, depending on where you run it. That's why this table belongs on every pull request.

Route to the right hardware

$ qont route --optimize value
┃ # ┃ Provider    ┃ Device          ┃ Est. success/shot ┃ Est. cost ┃ $ / success ┃
│ 1 │ AWS Braket  │ Rigetti Cepheus │             71.0% │    $10.00 │      $14.08 │
│ 2 │ IBM Quantum │ Heron (PAYG)    │             93.3% │    $16.01 │      $17.17 │

Cheap-but-noisy vs pricey-but-clean, resolved with one number.

Run on real hardware — without surprise bills

qont run --on ibm:ibm_brisbane --dry-run --max-cost 5    # estimate only
qont run --on braket:rigetti_cepheus --max-cost 5        # refuses if estimate > $5

The guard is all-or-nothing: the whole suite is priced before the first shot is submitted, and the default budget is $0.

Watch your suite over time

Every run appends to .qontinuum/history.jsonl; qont dashboard turns it into a single-file HTML dashboard — status timeline, each check's statistic trending against its threshold, cost per run. No server, no JavaScript, works as a CI artifact.

Diff circuits, not files

$ qont diff old.qasm new.qasm
- cx q[0, 1]
+ cx q[1, 0]
1 ops added, 1 removed

Register renames and formatting don't show up — only semantic changes do (the same canonicalization that powers snapshot staleness detection).

GitHub Action

# .github/workflows/quantum.yml
on: pull_request
permissions:
  contents: read
  pull-requests: write
jobs:
  quantum:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: XTanishkX/quantum/action@main
        with:
          path: .
          seed: "42"

Every PR gets one sticky comment (updated in place) with pass/fail per check and the hardware cost table — see a live one on PR #1.

Assertions

Assertion	What it tests
assert_distribution(result, expected, tvd_threshold=)	TVD against an expected distribution, with sampling-floor soundness check
assert_chi_squared(result, expected, alpha=)	Pearson goodness-of-fit
assert_fidelity(result, expected, min_fidelity=)	Hellinger (classical) fidelity
assert_probability(result, outcome, min_p=, max_p=)	Single-outcome probability bounds
assert_matches_baseline(result, counts, alpha=)	Two-sample test vs a recorded run

License

Apache-2.0