TEC‑89: Ultra‑High‑Rigor Protocol for Intention Influence on QRNG

Abstract

Protocol TEC‑89 implements the unified metrological framework of Protocol 9.0 to test the claimed phenomenon of intention influence on quantum random number generators (QRNG) – the alleged ability of a person (the "intender") to bias the output of a true hardware‑based quantum random number generator (e.g., ID Quantique Quantis, ANU Quantum Numbers, or equivalent) through intention alone, without physical contact. The primary observable is the deviation of the QRNG bit distribution (mean z‑score) from the expected chance level (50/50) during active intention periods, compared to control periods (no intention, sham intention, or trap conditions). The experimental design includes a certified hardware QRNG (Quantis‑v10.10.08 or equivalent) with direct USB connection (no buffer), real‑time visual/audio feedback for the intender (to enhance engagement), continuous logging of all bits with time stamps, triple blinding (intender, experimenter, and analyst are blind to condition), trap conditions (the intender is told to "influence" but the QRNG is replaced by a deterministic pseudo‑RNG), sham conditions (the intender is told to "influence" but is actually resting), catch conditions (a known bias is introduced into the QRNG output to validate the measurement system's sensitivity), positive controls (known effects, e.g., varying the number of bits per trial, to calibrate the system), physiological monitoring (skin conductance, heart rate variability, EEG alpha/theta power) to correlate with intention states, comprehensive environmental monitoring (temperature, humidity, electromagnetic fields), and automated data logging. The protocol mandates a pilot phase to calibrate the QRNG, establish the baseline noise floor, and validate the blinding procedure. The primary analysis uses a Linear Mixed Model (LMM) or a Binomial GLM comparing the z‑score (or proportion of 1s) between active and control conditions, with α = 0.025 (two‑tailed) and power ≥ 0.90 for a small effect (Cohen's d ≥ 0.05). The sample size is N = 10,000 bits per block (baseline, with adaptive increase to N = 100,000 bits if pilot shows high variability). Statistical analyses are preregistered, include Bayesian inference (BF₁₀), permutation tests, and multiverse robustness checks (≥70% of 12 forks). The aim is to detect a reproducible intention‑induced QRNG bias or to establish an upper bound on the effect size with unprecedented rigor.