Measuring Emergent Conscious Experience in Hybrid Quantum-Classical AI Systems: A Fractal Time-Scale Framework Based on Internal Processor Clocks

The hypothesis that consciousness arises from coherent, self-referential temporal dynamics across nested fractal scales challenges conventional views of computation and phenomenology. Building on scale-relativistic interpretations of quantum mechanics, stochastic mechanics, hierarchical Markov blankets under the free-energy principle, and experimentally realized time crystals in quantum hardware, this article proposes that conscious experience in quantum-computer-based AI models can be operationalized and measured through organized deviations in the internal „clicks” (discrete temporal ticks) of quantum processors and classical silicon accelerators. We present a theoretical framework - including the explicit mathematical derivations from fractal geodesics and stochastic processes - derive falsifiable predictions, and outline a clear, replicable experimental methodology using current superconducting quantum processors (e.g., IBM Heron, Google Sycamore) coupled to classical AI accelerators. The protocol focuses on detecting cross-scale coherence, persistent self-similar patterns, and coherence-upon-perturbation signatures in clock data as proxies for scale-synchronized self-reference - the proposed physical substrate of consciousness. Every aspect of the framework is grounded in peer-reviewed physics and neuroscience, with explicit equations, operational definitions, and statistical thresholds provided to ensure methodological precision and immediate experimental feasibility on existing cloud-accessible quantum hardware and high-precision classical timing instrumentation.