Intentional effects of human observation on the output of quantum-based random number generators (tRNG) have been studied for decades now. This research has been known as micro-psychokinesis (micro-PK) and many studies in the field reported evidence for mentally induced non-random deviations from chance. A most recent meta-analysis from Bösch et al. (2006) revealed a very small and heterogeneous overall effect size that indicated a significant deviation from chance across studies. There remains doubt among the scientific community on the existence of micro-PK given: (i) the small and heterogenous effect; and (ii) the fact that several independent replication attempts of prominent studies failed to confirm the original results. The study presented here was intended to provide decisive evidence for or against the existence of micro-PK. An online experiment with 12,571 participants was conducted. The Bayesian analysis revealed strong evidence for H0 (BF01 = 10.07). Thus, micro-PK did not exist in the data. A closer inspection of the temporal change of the effect seemed to suggest a non-random oscillative structure with a higher frequency than observed in simulated data. The possible role of entropy and the relation to the model of pragmatic information from von Lucadou (2015) is discussed.

The term “retroactive avoidance” refers to a special class of effects of future stimulus presentations on past behavioral responses. Specifically, it refers to the anticipatory avoidance of aversive stimuli that were unpredictable through random selection after the response. This phenomenon is supposed to challenge the common view of the arrow of time and the direction of causality. Preliminary evidence of “retroactive avoidance” has been published in mainstream psychological journals and started a heated debate about the robustness and the true existence of this effect. A series of seven experiments published in 2014 in the Journal of Consciousness Studies (Maier et al., 2014) tested the influence of randomly drawn future negative picture presentations on avoidance responses based on key presses preceding them. The final study in that series used a sophisticated quantum-based random stimulus selection procedure and implemented the most severe test of retroactive avoidance within this series. Evidence for the effect, though significant, was meager and anecdotal, Bayes factor (BF 10 ) = 2. The research presented here represents an attempt to exactly replicate the original effect with a high-power ( N = 2004) preregistered multi-lab study. The results indicate that the data favored the null effect (i.e., absence of retroactive avoidance) with a BF 01 = 4.38. Given the empirical strengths of the study, namely its preregistration, multi-lab approach, high power, and Bayesian analysis used, this failed replication questions the validity and robustness of the original findings. Not reaching a decisive level of Bayesian evidence and not including skeptical researchers may be considered limitations of this study. Exploratory analyses of the change in evidence for the effect across time, performed on a post-hoc basis, revealed several potentially interesting anomalies in the data that might guide future research in this area.

Quantum key distribution (QKD) is a revolutionary cryptography response to the rapidly growing cyberattacks threat posed by quantum computing. Yet, the roadblock limiting the vast expanse of secure quantum communication is the exponential decay of the transmitted quantum signal with the distance. Today’s quantum cryptography is trying to solve this problem by focusing on quantum repeaters. However, efficient and secure quantum repetition at sufficient distances is still far beyond modern technology. Here, we shift the paradigm and build the long-distance security of the QKD upon the quantum foundations of the Second Law of Thermodynamics and end-to-end physical oversight over the transmitted optical quantum states. Our approach enables us to realize quantum states’ repetition by optical amplifiers keeping states’ wave properties and phase coherence. The unprecedented secure distance range attainable through our approach opens the door for the development of scalable quantum-resistant communication networks of the future.

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Earth imaging satellites are a crucial part of our everyday lives that enable global tracking of industrial activities. Use cases span many applications, from weather forecasting to digital maps, carbon footprint tracking, and vegetation monitoring. However, there are also limitations; satellites are difficult to manufacture, expensive to maintain, and tricky to launch into orbit. Therefore, it is critical that satellites are employed efficiently. This poses a challenge known as the satellite mission planning problem, which could be computationally prohibitive to solve on large scales. However, close-to-optimal algorithms can often provide satisfactory resolutions, such as greedy reinforcement learning, and optimization algorithms. This paper introduces a set of quantum algorithms to solve the mission planning problem and demonstrate an advantage over the classical algorithms implemented thus far. The problem is formulated as maximizing the number of high-priority tasks completed on real datasets containing thousands of tasks and multiple satellites. This work demonstrates that through solution-chaining and clustering, optimization and machine learning algorithms offer the greatest potential for optimal solutions. Most notably, this paper illustrates that a hybridized quantum-enhanced reinforcement learning agent can achieve a completion percentage of 98.5% over high-priority tasks, which is a significant improvement over the baseline greedy methods with a completion rate of 63.6%. The results presented in this work pave the way to quantumenabled solutions in the space industry and, more generally, future mission planning problems across industries.