Identity-Locked Quantum Circuit Stabilization
Clamp noisy circuits into a deterministic, identity-locked attractor.
Q-Lock is a pre-processing engine that applies tiny, structured, identity-driven perturbations to your quantum circuits — preserving algorithmic intent while stabilizing behavior across noise, layout changes, and repeated runs.
Designed for teams who need provenance, auditability, and stability on real hardware — not just clean simulator demos.
Q-LOCK ATTRACTOR ENGINE
What Q-Lock actually does
Q-Lock is a pre-processing layer for quantum circuits that ingests an identity string and a circuit, computes a high-dimensional identity signature, and applies a tiny, structured perturbation to rotation gates — preserving logic while stabilizing behavior.
Identity encoding
A simple string like "team-a-prod-key" or
"alice@example.com" is hashed and expanded into a
high-dimensional real vector. This becomes the seed for the
attractor.
Latent-space attractor
The identity vector is mapped into a large latent space and passed through a structured, unitary-style attractor iteration with golden-ratio-inspired phase structure and controlled contraction.
Circuit fusion & lock
Circuit features and the identity signature vector are fused to compute small, per-gate perturbations. The result is a logically-equivalent, identity-locked circuit ready for simulation or hardware execution.
Why enterprises care
Q-Lock gives quantum programs something they usually lack: deterministic identity locking and distribution-aware stability under real-world noise.
Deterministic identity locking
Every locked circuit is tied to a cryptographic identity hash, providing provenance, auditability, and non-repudiation flavor without classical tokens or quantum keys.
Distribution-preserving behavior
On ideal simulators, output distributions are effectively unchanged. Under realistic noise, early tests often show more stable histograms and reduced sensitivity to compilation changes.
Hardware-aware architecture
Earlier versions have been exercised on IBM hardware (Perth, Brisbane, Toronto) with GHZ chains, entangling ladders, and parameterized networks — showing strong agreement with ideal distributions and stable behavior across repeated runs.
Architecture at a glance
Built to slot cleanly into modern quantum workflows, while keeping the core attractor logic proprietary inside AttraQtor Labs.
Public components
- Identity string input via CLI or notebook.
- QASM 2.0 and optional
QuantumCircuitintake. - Locked
QuantumCircuitoutput + QASM2 export. - Optional local simulation with QASM simulator for counts and baseline comparisons.
Private core
- High-dimensional latent-space attractor iteration.
- Golden-ratio-inspired phase and contraction structure.
- Fusion logic from identity signature to gate perturbations.
- Enterprise-ready modular design for future licensing.
Quickstart workflow
The current public engine is notebook-first, with a future packaged API planned for direct integration.
Notebook flow
- Open
q_lock_attractor_engine.ipynb. - Run the setup cell to install dependencies.
- Enter your identity string when prompted.
- Paste a QASM 2.0 circuit, then type
END. - Inspect original vs. locked circuit diagrams and histograms.
Planned Python API
from attraqtor_engine import QLockEngine
from qiskit import QuantumCircuit
engine = QLockEngine(identity="Prof. Einstein")
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.cx(0, 1)
qc.measure([0, 1], [0, 1])
locked_qc = engine.lock(qc)The API will mirror the notebook semantics: identity in, circuit in, locked circuit out.
Talk to AttraQtor Labs
Q-Lock is in active development. We’re working with a small number of teams who care deeply about stability, provenance, and identity in quantum workloads.
Enterprise & research inquiries
If you’re running experiments on real hardware, operating internal quantum platforms, or building quantum-adjacent products, we’d like to hear from you.
- WebsiteAttraQtorLabs.com
- GitHub@AttraQter-Labs
- Emailnic_hensley@proton.me
Current roadmap highlights
- Packaged
src/module and pip distribution. - Automated fidelity tests with Qiskit Aer noise models.
- Cross-provider hardware benchmarks.
- Formal whitepaper and public docs site.
- Optional enterprise licensing hooks.