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Name: qiskit-quantum-backend-selector
Version: 0.1.0
Summary: Noise- and queue-aware backend selection and routing for IBM Quantum backends
Author: Rex Rowan
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Project-URL: Homepage, https://github.com/RexRowan/Qiskit-Quantum-Backend-Selector
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: qiskit<3,>=2.0
Requires-Dist: qiskit-ibm-runtime<1,>=0.30
Provides-Extra: test
Requires-Dist: pytest>=7.0; extra == "test"
Provides-Extra: validate
Requires-Dist: qiskit-aer>=0.13; extra == "validate"
Dynamic: license-file

# Qiskit Quantum Backend Selector

[![Tests](https://github.com/RexRowan/Qiskit-Quantum-Backend-Selector/actions/workflows/tests.yml/badge.svg)](https://github.com/RexRowan/Qiskit-Quantum-Backend-Selector/actions/workflows/tests.yml)

Noise- and queue-aware backend selection and routing for IBM Quantum
backends, built on `qiskit-ibm-runtime`.

`QiskitRuntimeService.least_busy()` only looks at queue depth. This
package scores candidate backends per-circuit — factoring in whether the
circuit actually fits, and (optionally) an estimate of expected fidelity
after transpilation — then can route job submission with automatic
failover if a backend errors out.

## Install

```bash
pip install qiskit-quantum-backend-selector
```

Or from source:

```bash
pip install -e .
```

## Quick start

```python
from qiskit import QuantumCircuit
from qiskit_ibm_runtime import QiskitRuntimeService
from qiskit_backend_selector import BackendSelector, HybridScoring

service = QiskitRuntimeService()
selector = BackendSelector(service=service, strategy=HybridScoring())

qc = QuantumCircuit(2, 2)
qc.h(0)
qc.cx(0, 1)
qc.measure([0, 1], [0, 1])

best_backend = selector.select(qc)
```

With automatic failover on submission errors:

```python
from qiskit_ibm_runtime import SamplerV2 as Sampler
from qiskit_backend_selector import BackendRouter

router = BackendRouter(selector, max_attempts=3)

def submit(backend, circuit):
    sampler = Sampler(mode=backend)
    return sampler.run([circuit])

job = router.submit(qc, submit)
```

## Scoring strategies

- **`QueueOnlyScoring`** — ranks by reported pending-job count. Cheap,
  equivalent in spirit to `least_busy()`.
- **`NoiseAwareScoring`** — transpiles the circuit against each candidate
  backend's real coupling map and basis gates, then estimates fidelity by
  multiplying per-gate and per-readout error rates from `backend.properties()`
  along the transpiled circuit.
- **`HybridScoring`** — weighted sum of the two above (`queue_weight`,
  `noise_weight`, defaults 0.4/0.6).

Any strategy that implements `score(backend, circuit) -> float | None`
can be dropped in; returning `None` excludes a backend (e.g. it doesn't
have enough qubits for the circuit).

`QueueOnlyScoring`, `NoiseAwareScoring`, and `HybridScoring` all accept
an optional `CalibrationCache` (see `monitor.py`) to avoid refetching
`backend.status()`/`backend.properties()` on every call when scoring
many circuits in a short window:

```python
from qiskit_backend_selector import HybridScoring, CalibrationCache

queue_cache = CalibrationCache(ttl_seconds=30)     # queue depth changes fast
noise_cache = CalibrationCache(ttl_seconds=3600)   # calibration data doesn't
strategy = HybridScoring(queue_cache=queue_cache, noise_cache=noise_cache)
```

## Live smoke test

`scripts/live_smoke_test.py` exercises this against a real
`QiskitRuntimeService` — real auth, real calibration data, an actual job
submission — none of which the fake-backend test suite covers. Run it
manually with your own IBM Quantum credentials configured:

```bash
python scripts/live_smoke_test.py
```

This has been **run twice against a live service (2026-09-03)**. First
run caught a real bug (non-ISA circuit submission — see below, fixed in
`router.py`); second run, after the fix, submitted cleanly to `ibm_fez`
and returned a real `RuntimeJobV2`. Re-run after any further change to
`router.py` or `scoring.py`.

## Fidelity estimate: validation status

`scripts/validate_fidelity_estimate.py` checks `NoiseAwareScoring`'s
analytic prediction against a Qiskit Aer noise-model simulation built from
the same calibration data (`AerSimulator.from_backend`). Sample results:

| Circuit / backend       | Predicted | Simulated | Diff    |
|--------------------------|-----------|-----------|---------|
| bell_pair / Manila        | 0.9351    | 0.9395    | -0.0044 |
| bell_pair / Sherbrooke     | 0.9565    | 0.9602    | -0.0037 |
| ghz3 / Manila             | 0.8332    | 0.8397    | -0.0066 |
| ghz5 / Sherbrooke          | 0.8805    | 0.8869    | -0.0064 |
| ghz4 / Manila             | 0.8119    | 0.8292    | -0.0174 |

Agreement is within ~0.02 across these cases, and `tests/test_fidelity_validation.py`
regression-tests this stays under a 0.05 tolerance (skipped automatically
if the optional `qiskit-aer` extra isn't installed: `pip install -e ".[validate]"`).

**Read this narrowly.** Aer's noise model is built from the *same*
calibration numbers `NoiseAwareScoring` reads, using thermal-relaxation and
depolarizing channels per gate — which are themselves built from
independence assumptions similar to the analytic estimate's. Close
agreement here shows the analytic math is a reasonable approximation *of
that noise model*, not that the noise model matches a real device on any
given day. It does not touch crosstalk, calibration drift, or correlated
errors — the gap flagged below still stands until validated against an
actual hardware run.

## Known limitations

- **Independent-error assumption.** `NoiseAwareScoring` multiplies gate
  and readout error rates as if they were independent. Checked against
  Aer noise-model simulation (see above) it tracks within ~0.02 — but
  that simulation shares the same independence assumptions, so this
  doesn't rule out systematic overstatement on real hardware, where
  crosstalk, drift, and spatially correlated errors aren't Markovian
  per-gate channels. Treat it as a *relative* ranking signal until
  checked against real device results.
- **Transpile cost.** Noise-aware scoring transpiles the circuit against
  every candidate backend on every call. `CalibrationCache` (now wired in)
  avoids refetching `status()`/`properties()`, but transpilation itself
  isn't cached — for large batches of circuits against many backends this
  still gets expensive.
- **Queue score ignores job cost.** `QueueOnlyScoring` counts pending jobs,
  not their size or expected runtime, so it can't tell a queue of five
  quick jobs from a queue of five expensive ones.
- **Hybrid weights are defaults, not tuned.** The 0.4/0.6 split in
  `HybridScoring` is a reasonable starting point, not the result of
  empirical calibration against real turnaround-time or fidelity data.
- **No live-service integration tests in CI.** `scripts/live_smoke_test.py`
  has been run manually twice: once caught a real bug (non-ISA circuit
  submission, fixed — see above), once confirmed the fix works end-to-end
  against real hardware (`ibm_fez`, job `daciemu42tqs73as7h10`). It still
  isn't run automatically, so nothing currently prevents a future
  regression from reaching a release without someone running it manually.
  `BackendRouter`'s failover path itself is still verified only against
  injected `submit_fn`s in pytest, not real Runtime error modes like auth
  failures, rate limits, or mid-job backend retirement.
- **Static circuit assumption.** Scoring assumes a fixed circuit width
  known ahead of time. Dynamic circuits with mid-circuit measurement and
  classical feedforward are transpiled and scored the same way, but the
  fidelity estimate doesn't account for any additional overhead specific
  to dynamic execution.

## License

Apache-2.0
