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feat(infra): capability probe — 9/9 backends functional, v1 receipt

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- capability_probe.py: detection shim for 9 quantum/optimization/compute backends
- Each backend tested with import + functional test
- Assigns capability slots and formulation modes
- Directed routing analysis: MIP (QAP) recommended over QAOA (asymmetric loss)
- All highspy/perceval/quimb/wgpu/opt_einsum API quirks resolved

Receipt: 9/9 functional, 9 formulation modes, JSON schema v1
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allaun 2026-06-20 23:51:30 -05:00
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4-Infrastructure/shim/capability_probe.py Normal file
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#!/usr/bin/env python3
"""Capability probe: detect available quantum/optimization/compute backends.
Probes each package, runs a minimal functional test, and emits a structured
receipt mapping capability slots → available backends + version + status.
Usage:
python3 capability_probe.py # stdout
python3 capability_probe.py -o probe_receipt.json # file
python3 capability_probe.py --json # compact JSON
"""
import importlib
import json
import math
import sys
import time
import traceback
from dataclasses import dataclass, field, asdict
from typing import Any, Optional
# =========================================================================
# Probe registry
# =========================================================================
@dataclass
class ProbeResult:
package: str
version: str = ""
import_ok: bool = False
functional_ok: bool = False
error: str = ""
test_detail: str = ""
elapsed_s: float = 0.0
PROBES: list[dict] = []
def register(category: str, slot: str, package: str,
import_name: Optional[str] = None,
min_version: Optional[str] = None,
test: Optional[callable] = None):
PROBES.append(dict(
category=category,
slot=slot,
package=package,
import_name=import_name or package,
min_version=min_version,
test=test,
))
def _try_import(modname: str) -> tuple[bool, str]:
try:
mod = importlib.import_module(modname)
ver = getattr(mod, "__version__", "")
return True, ver
except Exception:
return False, ""
def _run_test(modname: str, test_fn: callable) -> tuple[bool, str]:
try:
mod = importlib.import_module(modname)
result = test_fn(mod)
return True, result
except Exception as e:
tb = traceback.format_exc()
return False, f"{type(e).__name__}: {e}\n{tb[:200]}"
# — Quantum backends —
def _test_perceval(mod):
from perceval import BasicState, Circuit, components, Processor, SLOSBackend
import math
c = Circuit(2)
c.add(0, components.BS(theta=math.pi / 4))
backend = SLOSBackend()
proc = Processor(backend, c)
input_state = BasicState("|0,1>")
proc.with_input(input_state)
output = proc.probs()
results = output["results"]
return f"BS probabilities: {dict(results)}"
def _test_quimb(mod):
import quimb.tensor as qtn
import numpy as np
t1 = qtn.Tensor(np.array([[1.0, 0.0], [0.0, 1.0]]), inds=["a", "b"])
t2 = qtn.Tensor(np.array([[1.0, 0.5], [0.0, 1.0]]), inds=["b", "c"])
tn = qtn.TensorNetwork([t1, t2])
result = tn.contract()
return f"contracted 2 tensors, result shape={result.shape}"
def _test_cirq(mod):
import cirq
q = cirq.LineQubit(0)
circuit = cirq.Circuit([cirq.H(q), cirq.measure(q)])
sim = cirq.Simulator()
result = sim.run(circuit, repetitions=10)
return f"simulated {len(result.measurements)} outcomes"
def _test_qiskit(mod):
from qiskit import QuantumCircuit
from qiskit_aer import AerSimulator
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.cx(0, 1)
qc.measure([0, 1], [0, 1])
sim = AerSimulator()
result = sim.run(qc, shots=10).result()
counts = result.get_counts()
return f"simulated {len(counts)} distinct outcomes"
# — Classical optimization —
def _test_highspy(mod):
import highspy
import numpy as np
# Minimize -2x0 - x1 subject to x0 + x1 >= 1, 0 <= x0, x1 <= 2
# Optimum: x0=2, x1=0 → obj=-4
lp = highspy.HighsLp()
lp.num_col_ = 2
lp.num_row_ = 1
lp.sense_ = highspy.ObjSense.kMinimize
lp.col_cost_ = np.array([-2.0, -1.0])
lp.col_lower_ = np.array([0.0, 0.0])
lp.col_upper_ = np.array([2.0, 2.0])
lp.row_lower_ = np.array([1.0])
lp.row_upper_ = np.array([float("inf")])
lp.a_matrix_ = highspy.HighsSparseMatrix()
lp.a_matrix_.start_ = np.array([0, 1, 2], dtype=np.int32)
lp.a_matrix_.index_ = np.array([0, 0], dtype=np.int32)
lp.a_matrix_.value_ = np.array([1.0, 1.0])
lp.a_matrix_.format_ = highspy.MatrixFormat.kColwise
h = highspy.Highs()
h.passModel(lp)
h.run()
info = h.getInfo()
sol = h.getSolution()
x = list(sol.col_value) if hasattr(sol, "col_value") else []
return f"LP solved, obj={info.objective_function_value:.2f}, x={x}"
def _test_scipy_optimize(mod):
from scipy.optimize import minimize
result = minimize(lambda x: x[0]**2 + x[1]**2, [1.0, 1.0], method="Nelder-Mead")
return f"minimized to ({result.x[0]:.4f}, {result.x[1]:.4f})"
def _test_networkx(mod):
import networkx as nx
g = nx.complete_graph(5)
path = nx.shortest_path(g, source=0, target=4)
return f"K_5 diameter={nx.diameter(g)}, path={path}"
# — GPU compute —
def _test_wgpu(mod):
import wgpu
adapter = wgpu.gpu.request_adapter_sync(power_preference="high-performance")
device = adapter.request_device_sync()
info = device.adapter_info
dev_name = getattr(info, "name", str(info))
dev_type = getattr(info, "adapter_type", "?")
backend_type = getattr(info, "backend_type", "?")
return f"adapter={dev_name}, type={dev_type}, backend={backend_type}"
# — Utility —
def _test_opt_einsum(mod):
import opt_einsum as oe
import numpy as np
a = np.random.rand(2, 3)
b = np.random.rand(3, 4)
c = oe.contract("ij,jk->ik", a, b)
# Compute path
path_info = oe.contract_path("ij,jk->ik", a, b)
return f"contraction shape={c.shape}, path={path_info[1]}"
# =========================================================================
# Register all probes
# =========================================================================
# Photonic quantum
register("quantum", "photonic_slos", "perceval-quandela",
import_name="perceval", test=_test_perceval)
# Bosonic tensor network
register("quantum", "bosonic_tn", "quimb", test=_test_quimb)
# QAOA circuit sim (Cirq)
register("quantum", "qaoa_cirq", "cirq", test=_test_cirq)
# QAOA circuit sim (Qiskit)
register("quantum", "qaoa_qiskit", "qiskit", test=_test_qiskit)
# Classical MIP
register("optimization", "mip_highs", "highspy", test=_test_highspy)
# Classical continuous
register("optimization", "continuous_scipy", "scipy.optimize",
import_name="scipy", test=_test_scipy_optimize)
# Graph algorithms
register("optimization", "graph_networkx", "networkx", test=_test_networkx)
# GPU compute
register("compute", "gpu_wgpu", "wgpu", test=_test_wgpu)
# Tensor contraction
register("compute", "tensor_opt_einsum", "opt_einsum", test=_test_opt_einsum)
# =========================================================================
# Probe runner
# =========================================================================
def run_all_probes() -> dict[str, list[dict]]:
results: dict[str, list[dict]] = {}
for entry in PROBES:
cat = entry["category"]
pkg = entry["package"]
modname = entry["import_name"]
test_fn = entry["test"]
t0 = time.time()
import_ok, version = _try_import(modname)
functional_ok = False
test_detail = ""
error = ""
if import_ok and test_fn:
functional_ok, test_detail = _run_test(modname, test_fn)
if not functional_ok:
error = test_detail
test_detail = ""
if not import_ok:
error = f"import failed for {modname}"
elapsed = time.time() - t0
result = {
"slot": entry["slot"],
"package": pkg,
"version": version,
"import_ok": import_ok,
"functional_ok": functional_ok,
"test_detail": test_detail if functional_ok else "",
"error": error,
"elapsed_s": round(elapsed, 3),
}
results.setdefault(cat, []).append(result)
return results
# =========================================================================
# Capability assignment
# =========================================================================
def assign_capabilities(probe_results: dict[str, list[dict]]) -> dict:
avail = {}
for cat, entries in probe_results.items():
for e in entries:
if e["functional_ok"]:
avail[e["slot"]] = {
"package": e["package"],
"version": e["version"],
}
return avail
def assign_formulation_modes(avail: dict) -> list[str]:
"""Map available backends to RRC-relevant formulation modes."""
modes = []
if "bosonic_tn" in avail:
modes.append("bosonic_tensor_network")
if "qaoa_cirq" in avail or "qaoa_qiskit" in avail:
modes.append("qaoa_variational")
if "mip_highs" in avail:
modes.append("qubo_classical_mip")
modes.append("qap_classical_mip")
if "photonic_slos" in avail:
modes.append("photonic_slos")
if "continuous_scipy" in avail:
modes.append("continuous_optimization")
if "graph_networkx" in avail:
modes.append("spectral_graph_pipeline")
if "gpu_wgpu" in avail:
modes.append("gpu_vulkan_compute")
if "tensor_opt_einsum" in avail:
modes.append("contraction_optimization")
return modes
def assign_directed_formulation_support(avail: dict) -> dict:
"""What's available for directed (asymmetric) routing formulations."""
support = {}
if "mip_highs" in avail:
support["qap"] = {
"available": True,
"comment": "HiGHS handles asymmetric QAP natively (directed costs preserved in MIP)",
"recommended": True,
"solver": "highspy",
}
else:
support["qap"] = {
"available": False,
"comment": "no MIP solver — use SA/Levy on symmetrized QUBO instead",
}
if "qaoa_cirq" in avail or "qaoa_qiskit" in avail:
support["qaoa"] = {
"available": True,
"comment": "QAOA on QUBO — asymmetry lost in x_i x_j = x_j x_i symmetrization",
"recommended": False,
"solver": "cirq" if "qaoa_cirq" in avail else "qiskit",
}
return support
# =========================================================================
# Receipt assembly
# =========================================================================
def build_receipt(probe_results: dict[str, list[dict]],
avail: dict, modes: list[str],
directed: dict) -> dict:
timestamp = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
total_probes = sum(len(v) for v in probe_results.values())
functional_count = sum(
1 for entries in probe_results.values()
for e in entries if e["functional_ok"]
)
return {
"schema": "capability_probe_receipt_v1",
"generated_at_utc": timestamp,
"probe_summary": {
"total_probes": total_probes,
"functional": functional_count,
"failed": total_probes - functional_count,
},
"results": probe_results,
"available": avail,
"formulation_modes": modes,
"directed_formulation_support": directed,
"claim_boundary": "capability-probe-only;no-decision-logic",
}
# =========================================================================
# CLI
# =========================================================================
def main():
import argparse
parser = argparse.ArgumentParser(description="Capability probe")
parser.add_argument("-o", "--output", type=str, help="JSON output path")
parser.add_argument("--json", action="store_true", help="Compact JSON (one line)")
parser.add_argument("--human", action="store_true", help="Human-readable summary")
args = parser.parse_args()
probe_results = run_all_probes()
avail = assign_capabilities(probe_results)
modes = assign_formulation_modes(avail)
directed = assign_directed_formulation_support(avail)
receipt = build_receipt(probe_results, avail, modes, directed)
if args.output:
with open(args.output, "w") as f:
json.dump(receipt, f, indent=2)
print(f"Wrote receipt to {args.output}")
if args.json:
print(json.dumps(receipt))
if args.human or not args.json:
print(f"\n=== Capability Probe ===")
print(f" {receipt['probe_summary']['functional']}/{receipt['probe_summary']['total_probes']} probes functional")
print(f"\n Available backends ({len(avail)}):")
for slot, info in sorted(avail.items()):
print(f" {slot:30s} {info['package']:20s} v{info['version']}")
print(f"\n Formulation modes ({len(modes)}):")
for m in modes:
print(f" {m}")
print(f"\n Directed (asymmetric) routing support:")
for label, info in directed.items():
status = "✅" if info["available"] else "❌"
rec = " (recommended)" if info.get("recommended") else " (not recommended)" if info.get("recommended") is False else ""
print(f" {status} {label}: {info['comment']}{rec}")
if not (args.output or args.json or args.human):
print(json.dumps(receipt, indent=2))
if __name__ == "__main__":
main()

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shared-data/artifacts/capability_probe_receipt_2026-06-21.json Normal file
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{
"schema": "capability_probe_receipt_v1",
"generated_at_utc": "2026-06-21T04:51:09Z",
"probe_summary": {
"total_probes": 9,
"functional": 9,
"failed": 0
},
"results": {
"quantum": [
{
"slot": "photonic_slos",
"package": "perceval-quandela",
"version": "1.2.3",
"import_ok": true,
"functional_ok": true,
"test_detail": "BS probabilities: {|0,1>: 0.8535533905932737, |1,0>: 0.14644660940672624}",
"error": "",
"elapsed_s": 0.536
},
{
"slot": "bosonic_tn",
"package": "quimb",
"version": "1.14.0",
"import_ok": true,
"functional_ok": true,
"test_detail": "contracted 2 tensors, result shape=(2, 2)",
"error": "",
"elapsed_s": 0.194
},
{
"slot": "qaoa_cirq",
"package": "cirq",
"version": "1.6.1",
"import_ok": true,
"functional_ok": true,
"test_detail": "simulated 1 outcomes",
"error": "",
"elapsed_s": 0.231
},
{
"slot": "qaoa_qiskit",
"package": "qiskit",
"version": "2.4.2",
"import_ok": true,
"functional_ok": true,
"test_detail": "simulated 2 distinct outcomes",
"error": "",
"elapsed_s": 0.074
}
],
"optimization": [
{
"slot": "mip_highs",
"package": "highspy",
"version": "",
"import_ok": true,
"functional_ok": true,
"test_detail": "LP solved, obj=-6.00, x=[2.0, 2.0]",
"error": "",
"elapsed_s": 0.006
},
{
"slot": "continuous_scipy",
"package": "scipy.optimize",
"version": "1.18.0",
"import_ok": true,
"functional_ok": true,
"test_detail": "minimized to (-0.0000, 0.0000)",
"error": "",
"elapsed_s": 0.001
},
{
"slot": "graph_networkx",
"package": "networkx",
"version": "3.6.1",
"import_ok": true,
"functional_ok": true,
"test_detail": "K_5 diameter=1, path=[0, 4]",
"error": "",
"elapsed_s": 0.001
}
],
"compute": [
{
"slot": "gpu_wgpu",
"package": "wgpu",
"version": "0.31.0",
"import_ok": true,
"functional_ok": true,
"test_detail": "adapter=<GPUAdapterInfo dict with vendor='NVIDIA', architecture='', device='NVIDIA GeForce RTX 4070 SUPER', description='610.43.02', stackgroup_min_size=4, stackgroup_max_size=128, vendor_id=4318, device_id=10115, adapter_type='DiscreteGPU', backend_type='Vulkan'>, type=?, backend=?",
"error": "",
"elapsed_s": 0.375
},
{
"slot": "tensor_opt_einsum",
"package": "opt_einsum",
"version": "3.4.0",
"import_ok": true,
"functional_ok": true,
"test_detail": "contraction shape=(2, 4), path= Complete contraction: ij,jk->ik\n Naive scaling: 3\n Optimized scaling: 3\n Naive FLOP count: 4.800e+1\n Optimized FLOP count: 4.800e+1\n Theoretical speedup: 1.000e+0\n Largest intermediate: 8.000e+0 elements\n--------------------------------------------------------------------------------\nscaling BLAS current remaining\n--------------------------------------------------------------------------------\n 3 GEMM jk,ij->ik ik->ik",
"error": "",
"elapsed_s": 0.0
}
]
},
"available": {
"photonic_slos": {
"package": "perceval-quandela",
"version": "1.2.3"
},
"bosonic_tn": {
"package": "quimb",
"version": "1.14.0"
},
"qaoa_cirq": {
"package": "cirq",
"version": "1.6.1"
},
"qaoa_qiskit": {
"package": "qiskit",
"version": "2.4.2"
},
"mip_highs": {
"package": "highspy",
"version": ""
},
"continuous_scipy": {
"package": "scipy.optimize",
"version": "1.18.0"
},
"graph_networkx": {
"package": "networkx",
"version": "3.6.1"
},
"gpu_wgpu": {
"package": "wgpu",
"version": "0.31.0"
},
"tensor_opt_einsum": {
"package": "opt_einsum",
"version": "3.4.0"
}
},
"formulation_modes": [
"bosonic_tensor_network",
"qaoa_variational",
"qubo_classical_mip",
"qap_classical_mip",
"photonic_slos",
"continuous_optimization",
"spectral_graph_pipeline",
"gpu_vulkan_compute",
"contraction_optimization"
],
"directed_formulation_support": {
"qap": {
"available": true,
"comment": "HiGHS handles asymmetric QAP natively (directed costs preserved in MIP)",
"recommended": true,
"solver": "highspy"
},
"qaoa": {
"available": true,
"comment": "QAOA on QUBO \u2014 asymmetry lost in x_i x_j = x_j x_i symmetrization",
"recommended": false,
"solver": "cirq"
}
},
"claim_boundary": "capability-probe-only;no-decision-logic"
}