Project WHISPER is a preregistered experimental protocol testing whether statistical properties of quantum random number generator (QRNG) output recorded prior to a future event are independent of a later, independently generated target bit. The study specifies a single, falsifiable hypothesis: that summary statistics computed on a fixed pre-event window of QRNG data are either independent of, or systematically biased toward, a future target value generated by an independent QRNG. All hypotheses, analysis windows, statistical tests, and significance thresholds are preregistered prior to data unblinding. The protocol is designed to eliminate classical information leakage, experimenter bias, and analytical flexibility. A null result supports standard quantum mechanical assumptions of temporal independence; a positive result would motivate independent replication and further investigation.
Project WHISPER is a preregistered experimental protocol testing whether statistical properties of quantum random number generator (QRNG) output recorded prior to a future event are independent of a later, independently generated target bit. The study specifies a single, falsifiable hypothesis: that summary statistics computed on a fixed pre-event window of QRNG data are either independent of, or systematically biased toward, a future target value generated by an independent QRNG. All hypotheses, analysis windows, statistical tests, and significance thresholds are preregistered prior to data unblinding. The protocol is designed to eliminate classical information leakage, experimenter bias, and analytical flexibility. A null result supports standard quantum mechanical assumptions of temporal independence; a positive result would motivate independent replication and further investigation.
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.
ABSTRACT Quantum random number generator (QRNG) platforms use principles of quantum mechanics to create truly random numbers. Traditional pseudo-random number generators (PRNGs) generate numbers based on deterministic formulas. QRNG's use of the principles of superposition and measurement can provide numbers that cannot be predicted or replicated; therefore, they have been used as a source of randomness in various fields. In this research project, an experimental QRNG was designed and built using Qiskit simulations. Each experiment consisted of a series of two-qubit circuits. The circuits were created to place both qubits into an equal probability state. This was done by applying a pair of Hadamard gates (Hadamard gate applies a rotation operator of π/2 radians). Then each circuit was measured. For each measurement, there would be one output from each qubit. Since the Hadamard gate creates an equal-probability superposition, the combination of each qubit will either add up to 1 or subtract from -1. These combinations will be recorded and processed through several methods to evaluate their quality. Those methods include: Shannon Entropy Analysis, Frequency Distribution Analysis and Chi-Square Uniformity Testing. Keywords: Quantum Random Number Generator, Quantum Computing, Quantum Measurement, Cryptography, Secure Randomness, Qiskit Simulation
Quantum random number generators (QRNGs) offer a fundamentally different approach to random number generation compared to classical methods, leveraging the inherent randomness of quantum processes. However, their practical implementation and analysis often face challenges, particularly concerning the accurate characterization of the underlying probability distribution and the optimization of generation algorithms. This paper presents a comprehensive probability analysis of QRNG output, focusing on identifying and quantifying sources of bias and uncertainty. We introduce a novel framework combining quantum information theory and statistical analysis to rigorously evaluate QRNG performance. Furthermore, we propose an optimization strategy based on adaptive sampling techniques and error correction, aiming to enhance both the security and efficiency of QRNGs. Our approach provides a detailed understanding of QRNG behavior and offers actionable insights for designing and deploying robust QRNG systems. The core of our analysis lies in the precise determination of the output probability distribution, denoted as P(s1, s2, ..., sK), where K represents the number of bits generated. We demonstrate how this distribution can be refined through iterative statistical estimation and how this information can be used to improve the generation process.
Even if the output of a Random Number Generator (RNG) is perfectly uniformly distributed, it may be correlated to pre-existing information and therefore be predictable. Statistical tests are thus not sufficient to guarantee that an RNG is usable for applications, e.g., in cryptography or gambling, where unpredictability is important. To enable such applications a stronger notion of randomness, termed "true randomness", is required, which includes independence from prior information. Quantum systems are particularly suitable for true randomness generation, as their unpredictability can be proved based on physical principles. Practical implementations of Quantum RNGs (QRNGs) are however always subject to noise, i.e., influences which are not fully controlled. This reduces the quality of the raw randomness generated by the device, making it necessary to post-process it. Here we provide a framework to analyse realistic QRNGs and to determine the post-processing that is necessary to turn their raw output into true randomness.
The question of human free will has remained a central philosophical challenge for millennia. In physics, the issue of determinism versus indeterminism resurfaces in quantum mechanics, where measurement choices play a key role. In this work, we propose an experimental protocol inspired by delayed-choice quantum eraser experiments, in which the random switching of measurement settings is not performed by a quantum random number generator (QRNG), but instead by human participants making rapid spontaneous choices. If human choices are fundamentally unpredictable in a way that is not reducible to quantum mechanics or physical determinism, then the universal wave function could not have encoded them prior to the decision event. By introducing quantum memories to extend the decision window and ensuring strict separation and blindness of participants, this experiment could test whether interference patterns depend on genuinely free human choices or yield results identical to QRNG-based protocols.