The functioning of complex natural systems, such as living systems, still lacks a generally accepted theoretical basis and respective empirical verification. We propose an experiment to test whether such systems are subject to a hitherto unknown organizing principle, in which the quantum mechanical uncertainty principle enables ordering phenomena in chaotic or nearly chaotic systems in the sense of a strong emergence principle. Contrary to previous proposals, our hypothesis does not require fragile macroscopic quantum coherence, ensuring compatibility with the harsh conditions in living systems. To test this hypothesis, we suggest comparing two virtually identical, complex artificial neural networks as a quantitative benchmark. One setup utilizes deterministic pseudo-random number generators at key sensitive points, while the other uses quantum-based physical random-number generators. A favorable outcome demonstrating the superiority of quantum randomness would have considerable implications beyond artificial networks, particularly for the discussions on consciousness and free will.
The functioning of complex natural systems, such as living systems, still lacks a generally accepted theoretical basis and respective empirical verification. We propose an experiment to test whether such systems are subject to a hitherto unknown organizing principle, in which the quantum mechanical uncertainty principle enables ordering phenomena in chaotic or nearly chaotic systems in the sense of a strong emergence principle. Contrary to previous proposals, our hypothesis does not require fragile macroscopic quantum coherence, ensuring compatibility with the harsh conditions in living systems. To test this hypothesis, we suggest comparing two virtually identical, complex artificial neural networks as a quantitative benchmark. One setup utilizes deterministic pseudo-random number generators at key sensitive points, while the other uses quantum-based physical random-number generators. A favorable outcome demonstrating the superiority of quantum randomness would have considerable implications beyond artificial networks, particularly for the discussions on consciousness and free will.
The functioning of complex natural systems, such as living systems, still lacks a generally accepted theoretical basis and respective empirical verification. We propose an experiment to test whether such systems are subject to a hitherto unknown organizing principle, in which the quantum mechanical uncertainty principle enables ordering phenomena in chaotic or nearly chaotic systems in the sense of a strong emergence principle. Contrary to previous proposals, our hypothesis does not require fragile macroscopic quantum coherence, ensuring compatibility with the harsh conditions in living systems. To test this hypothesis, we suggest comparing two virtually identical, complex artificial neural networks as a quantitative benchmark. One setup utilizes deterministic pseudo-random number generators at key sensitive points, while the other uses quantum-based physical random-number generators. A favorable outcome demonstrating the superiority of quantum randomness would have considerable implications beyond artificial networks, particularly for the discussions on consciousness and free will.
The functioning of complex natural systems, such as living systems, still lacks a generally accepted theoretical basis and respective empirical verification. We propose an experiment to test whether such systems are subject to a hitherto unknown organizing principle, in which the quantum mechanical uncertainty principle enables ordering phenomena in chaotic or nearly chaotic systems in the sense of a strong emergence principle. Contrary to previous proposals, our hypothesis does not require fragile macroscopic quantum coherence, ensuring compatibility with the harsh conditions in living systems. To test this hypothesis, we suggest comparing two virtually identical, complex artificial neural networks as a quantitative benchmark. One setup utilizes deterministic pseudo-random number generators at key sensitive points, while the other uses quantum-based physical random-number generators. A favorable outcome demonstrating the superiority of quantum randomness would have considerable implications beyond artificial networks, particularly for the discussions on consciousness and free will.
The functioning of complex natural systems, such as living systems, still lacks a generally accepted theoretical basis and respective empirical verification. We propose an experiment to test whether such systems are subject to an unknown ordering principle beyond known physical laws. We hypothesize that the quantum mechanical uncertainty principle enables ordering phenomena in nearly chaotic systems in the sense of a strong emergence principle. Contrary to previous proposals, our hypothesis does not require fragile macroscopic quantum coherence, ensuring compatibility with the harsh conditions in living systems. To test this hypothesis, we suggest comparing two virtually identical, complex artificial neural networks as a quantitative benchmark. One setup utilizes deterministic pseudo-random number generators at key sensitive points, while the other uses quantum-based physical random-number generators. A favorable outcome demonstrating the superiority of quantum randomness would have considerable implications beyond artificial networks, particularly for the discussions on consciousness and free will.
The functioning of complex natural systems, such as living systems, still lacks a generally accepted theoretical basis and respective empirical verification. We propose an experiment to test whether such systems are subject to an unknown ordering principle beyond known physical laws. We hypothesize that the quantum mechanical uncertainty principle enables ordering phenomena in nearly chaotic systems in the sense of a strong emergence principle. Contrary to previous proposals, our hypothesis does not require fragile macroscopic quantum coherence, ensuring compatibility with the harsh conditions in living systems. To test this hypothesis, we suggest comparing two virtually identical, complex artificial neural networks as a quantitative benchmark. One setup utilizes deterministic pseudo-random number generators at key sensitive points, while the other uses quantum-based physical random-number generators. A favorable outcome demonstrating the superiority of quantum randomness would have considerable implications beyond artificial networks, particularly for the discussions on consciousness and free will.
The vital characteristic of randomness is unpredictability. Thus any regularity will compromise the application of random numbers. Quantum random number generators (QRNGs) can provide intrinsic unpredictable randomness based on the nature of quantum physics, while pseudo-random number generator can not due to the origination of deterministic algorithms or physical processes. However, commonly used traditional test suits are rigorously to test the statistical properties, and the unpredictability of random numbers is still difficult to test. To verify which sources of random numbers are truly unpredictable, in this paper, we propose a new randomness testing method with the artificial neural network (ANN). Random number sequences generated by four different kinds of sources are tested, which are the natural number λ, the linear congruence generator (LCG) pseudo-random algorithm, the Mersenne Twister (MT) pseudorandom algorithm and the QRNG based on vacuum noise, respectively. The testing results indicate that the random sequences from natural number λ and LCG fail to pass our randomness test, while the other two kinds of sequences pass the test successfully due to the relatively simple structure of our ANN. As the complexity of the ANN and the amount of computing power involved increasing, this test method has potential to predict the MT random numbers, and it is also expected that the quantum random numbers can not pass the test with unpredictability no matter how complex the ANN is.
This paper investigates the application of quantum-inspired neural networks (QINNs) to the simulation of chaotic systems. Traditional neural networks often struggle to capture the intricate dynamics of chaotic systems due to their inherent limitations in representing multiple states concurrently. This research proposes a novel architecture for QINNs that draws inspiration from quantum superposition and entanglement, enabling the network to simultaneously represent and propagate multiple states of the system. The core principle involves introducing a network structure where nodes can exist in multiple states, mirroring the quantum mechanical phenomenon of superposition. Mathematical formulations detail the network's architecture and training process, emphasizing the utilization of weighted sums and activation functions to simulate state transitions. The experimental results demonstrate that QINNs achieve significantly improved accuracy in simulating chaotic systems compared to conventional neural networks, particularly for systems with high dimensionality and complex dynamics. The paper concludes with a discussion of the potential implications of QINNs for various applications, including chaos control, cryptography, and financial modeling.