Quantum Probability Distribution Network

Sébastien Perseguers, Maciej Lewenstein, Antonio Acín +1 more

Jincheng Zhang

This paper investigates the development of an adaptive self-organized quantum network (ASQN) quantum probability distribution optimization algorithm. Traditional quantum computation relies heavily on human intervention in parameter tuning, leading to limitations in accuracy and efficiency. The proposed algorithm dynamically adjusts the probability distribution within the ASQN, automatically optimizing the quantum state for improved performance. The core mechanism involves a feedback loop utilizing system state to iteratively refine the probability distribution, offering a novel approach to quantum computing that surpasses existing optimization techniques. This work demonstrates the potential for significant improvements in quantum accuracy through automated adjustment, paving the way for more robust and reliable quantum computations.

Jincheng Zhang

This paper investigates a novel computational framework designed to automatically adjust probability distributions during the training of machine learning models. The framework leverages a dynamically evolving "quantum entanglement network" to optimize data distribution, thereby enhancing model prediction accuracy. We propose a method for continuously adjusting network connections based on data characteristics, resulting in a more adaptable and optimized learning process. This work addresses the limitations of traditional models reliant on fixed probability distributions, offering enhanced performance through dynamic distribution adjustment. The core mechanism centers around the creation and manipulation of a quantum entanglement network to guide the optimization process.

D.S. Wiersma

Bi Qiao, Fang Jin-Qing, Liu Gui-ping

Ding Dong, Yu-Yan Zhang, Zan-Jia Li +3 more

Abstract Quantum networks relying on entanglement swapping offer practical resources for quantum communication, generally assisted by Bell state measurements (BSM). In contrast to conventional BSM, the novel symmetric joint measurements not only subsume standard BSM but also feature parameterization advantages. We investigate the probability distribution of three-parameter symmetric joint measurements on a triangular network. A primary result is that the output probabilities generally depend on the quantum state distributed by the sources to the three parties. We consider four Bell states serving as initial states, exploring the permutation symmetry of the measurement probabilities while applying them to nonlocality tests in a triangular network. The results demonstrate that the three-parameter symmetric joint measurement is highly effective for network nonlocality testing. Specifically, when using three singlet states as the initial states, the network nonlocality is robustly exhibited regardless of the selection of measurement parameters z , ϕ , and θ .