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Adam Laabs

Publications and source records attributed to Adam Laabs.

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AlchemQ: Proof-Carrying Quantum Circuit Optimization with Per-Result Equivalence Certificates

We present AlchemQ v0.5, a proof-of-concept system that couples an untrusted beam-search optimizer with a machine-checkable per-result certification layer and a versioned certificate protocol (0.2.0), so that every optimized circuit ships with a verifiable artifact rather than a bare claim. The certifier proves equivalence up to global phase by ZX-calculus full reduction, with a numeric-tensor fallback based on the optimal Hilbert-Schmidt overlap. Certificates are self-contained and tamper-evident: canonical gate-canon-v1 hashes, measured residuals, tri-state verdicts (certified/rejected/inconclusive), and versioned phase-note schemas for cross-platform reproducibility. The agent aggregates three fuzzy t-norms, cannot return an uncertified circuit, and since v0.4 guarantees no componentwise regression against the original. On a benchmark of 100 circuits, all 400 optimizations terminate without error, every returned circuit is certified, every mutation is detected, and a 2998-test suite passes on two platforms. The PyZX baseline is strong (21.4% mean T-count reduction over 82 circuits) and the agent is strictly better on 9/100; the three t-norms return identical circuits on all 100 standard instances, diverging only on 4/38 of an adversarial suite. Two case studies are new: a false negative root-caused to a pivot-normalization bug in PyZX's compare_tensors (pivot 4.7e-9; the optimal-overlap residual is 7.4e-11), and eight certificates rejected on macOS due to BLAS-dependent floats in phase_note. Both were fixed; all artifact sets validate 400/400 on both platforms. A pilot run on IBM Heron r2 gives a certified circuit 78% shallower with 65% fewer two-qubit gates; output quality favors it on all three metrics but is not significant at 1024 shots. We release the certificate specification and a standalone reference verifier (Apache-2.0) with data and scripts; the engine is proprietary.

quant-ph

T-Norm Operators for EU AI Act Compliance Classification: An Empirical Comparison of Lukasiewicz, Product, and Gödel Semantics in a Neuro-Symbolic Reasoning System

We present a first comparative pilot study of three t-norm operators -- Lukasiewicz (T_L), Product (T_P), and Gödel (T_G) - as logical conjunction mechanisms in a neuro-symbolic reasoning system for EU AI Act compliance classification. Using the LGGT+ (Logic-Guided Graph Transformers Plus) engine and a benchmark of 1035 annotated AI system descriptions spanning four risk categories (prohibited, high_risk, limited_risk, minimal_risk), we evaluate classification accuracy, false positive and false negative rates, and operator behaviour on ambiguous cases. At n=1035, all three operators differ significantly (McNemar p<0.001). T_G achieves highest accuracy (84.5%) and best borderline recall (85%), but introduces 8 false positives (0.8%) via min-semantics over-classification. T_L and T_P maintain zero false positives, with T_P outperforming T_L (81.2% vs. 78.5%). Our principal findings are: (1) operator choice is secondary to rule base completeness; (2) T_L and T_P maintain zero false positives but miss borderline cases; (3) T_G's min-semantics achieves higher recall at cost of 0.8% false positive rate; (4) a mixed-semantics classifier is the productive next step. We release the LGGT+ core engine (201/201 tests passing) and benchmark dataset (n=1035) under Apache 2.0.

cs.AI