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Frontier AI Alignment | Macroscopic quantum coherence
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Frontier AI Alignment | Macroscopic quantum coherence
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christopher-altman/README.md

Starlab | Deep Future

Frontier AI evaluation · Quantum machine learning · Superconducting quantum benchmarks

Our research program builds falsification frameworks and reproducible evaluation harnesses for testing claims in frontier AI evaluation, alignment testing, quantum machine learning, and quantum hardware.

Current work focuses on structural AI metrics, continuation-risk measurement, quantum kernel methods, telemetry anomaly detection, and superconducting-qubit benchmarks.

Continuation Observatory   |   Frontier AI   |   Google Scholar   |   Homepage

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  1. persistence-signal-detector persistence-signal-detector Public

    A multi-criterion diagnostic framework for detecting latent continuation-interest signatures in autonomous agents using density-matrix entanglement entropy.

    Python 3

  2. qml-verification-lab qml-verification-lab Public

    Verification harness for quantum ML. A reproducible lab for stress-testing quantum models where predictive accuracy, identifiability, curvature, and robustness under noise can diverge.

    Python 2

  3. noise-aware-qnn-identifiability noise-aware-qnn-identifiability Public

    When performance survives noise, identifiability may not.

    Python 3

  4. qkernel-telemetry-anomaly qkernel-telemetry-anomaly Public

    Applied quantum kernels for anomaly detection. Low-data anomaly detection on manifold-structured telemetry, benchmarking entanglement kernels vs classical baselines with geometric diagnostics.

    Python 3

  5. ibm-qml-kernel ibm-qml-kernel Public

    Quantum kernel estimation with backend-matched IBM noise modeling, plus reproducible “Wigner’s friend” branch-transfer coherence-witness experiments executed on superconducting quantum hardware.

    Python 7

  6. sat-qkd-security-curves sat-qkd-security-curves Public

    Quantum keys can fail quietly. Loss and noise can leave you with bits, but no secrecy. We model the cliff to expose silent breakage before it becomes a system risk.

    Python 2