autoq-qec 0.2.1

Multi-code QEC resource estimator for arbitrary Qiskit circuits

AutoQ QEC Estimator

Multi-code fault-tolerant quantum error correction estimator for arbitrary Qiskit circuits.

Given any Qiskit circuit and a set of hardware profiles, AutoQ QEC returns a ranked comparison of QEC codes (Surface Code, Floquet Code, Bacon-Shor, Steane [[7,1,3]]) with physically grounded resource estimates: physical qubit count, execution time, and circuit fidelity.

What this does that nothing else does

Tool	Multi-code	Arbitrary circuit	Analytic model	Hardware-agnostic
Azure Resource Estimator	❌ Surface Code only	✅	✅	❌ Azure only
stim	✅	❌ needs rewrite	❌ simulation	❌
qiskit-qec	✅	❌ no estimator	❌	✅
AutoQ QEC	✅	✅	✅	✅

Install

pip install autoq-qec
# With IBM Quantum integration:
pip install "autoq-qec[ibm,sim]"

Quickstart

from qiskit import QuantumCircuit
from autoq_qec import compare, rank
from autoq_qec import HARDWARE_PROFILES, CalibratedHardware, HardwareProfile

# Any Qiskit circuit
circuit = QuantumCircuit(4)
circuit.h(0); circuit.cx(0,1); circuit.cx(1,2); circuit.cx(2,3)

# Hardware profiles (built-in or custom)
hardwares = [
    HardwareProfile("IBM_Eagle",     t_gate_ns=391,   p_phys=0.0062, topology="heavy-hex"),
    HardwareProfile("IBM_Heron",     t_gate_ns=100,   p_phys=0.003,  topology="heavy-hex"),
    HardwareProfile("Quantinuum_H2", t_gate_ns=100e3, p_phys=0.00029,topology="all-to-all"),
]

# One call — returns all codes × all hardwares
result = compare(circuit, hardwares, fidelity_target=0.99)

# Pass hardware_calibrations to exclude combinations that violate T1
# (t_circuit >= 0.5×T1) — matches by name or by (t_gate_ns, p_phys)
recommendations = rank(result, hardware_calibrations=HARDWARE_PROFILES)

for r in recommendations[:3]:
    print(f"#{r.rank} {r.hardware} + {r.code}: "
          f"{r.total_physical_qubits}q, {r.execution_time_us:.1f}µs, "
          f"fidelity={r.fidelity_circuit:.4f}")

With real IBM calibration data

from autoq_qec.real_hardware import from_ibm_backend, noise_model_from_ibm

# Pulls today's calibration — T1, T2, CX error per qubit pair
hw = from_ibm_backend("ibm_brisbane", token="YOUR_IBM_TOKEN")

# Simulate locally with real noise model (no queue, no cost)
sim = noise_model_from_ibm("ibm_brisbane", token="YOUR_IBM_TOKEN")

Physical models

Code	Model	Reference
Surface Code	$p_L \approx A(p/p_{th})^{(d+1)/2}$, $q=2d^2-1$, overhead $=d^3$	Fowler et al., PRA 86, 032324 (2012)
Floquet Code	$p_L \approx 0.07(p/p_{th})^{(d+1)/2}$, $q=4d^2+8(d-1)$, overhead $=\lfloor d/2\rfloor$	Gidney & Fowler, arXiv:2202.11829
Bacon-Shor	$p_L \approx (p/p_{th})^d$, $q=d^2$	Aliferis & Cross (2007)
Steane [[7,1,3]]	$p_L \approx 21p^2$, $q=13$	Steane, PRL 77, 793 (1996)

Thresholds are enforced: p ≥ p_th raises ValueError — no silent wrong results.

Algorithm Estimator

Order-of-magnitude T-count estimates for known algorithms, without building the full circuit:

from autoq_qec import AlgorithmEstimator

est = AlgorithmEstimator.shor(2048)
print(est.t_count_estimate, est.t_count_uncertainty)  # ±5x — build the real circuit for precise numbers

Covers shor, grover, qft, vqe. These are rough estimates (±2×–±10× depending on the algorithm) — use extract_circuit_profile() on a real circuit whenever possible.

Test

pytest tests/ -v   # 49 tests, all verify physics not arithmetic

What the tests check (unlike most QEC tools)

• p ≥ threshold raises ValueError, not wrong overhead
• d is always odd for Surface Code (rotated lattice requirement)
• p_L ≤ p_L_target guaranteed after distance selection
• Noisier hardware requires larger d (monotonicity)
• Circuit with 0 gates raises ValueError (destroyed by transpiler)
• Fidelity scales correctly with t_gate

Author

Ronaldo Rodrigues — ORCID: 0009-0006-7449-1190