Search

Quantum networks are interconnected by nodes, between singlets which are formed to ensure the successful transmission of information with a probability of 1. However, in real quantum networks, nodes often share a partially entangled state instead of a singlet due to factors such as environmental noise. Therefore, it is necessary to convert the partially entangled state into a singlet for efficient communication. Percolation happens during the conversion of connected edges in the whole network. As a result, when the singlet conversion probability (SCP) is greater than the percolation threshold, a giant interconnected cluster that meets the basic requirements of communication will appear in the network. The percolation threshold of the network reveals the minimum resources required to carry out large scale quantum communication. In this paper, we investigate the effect of different q-swaps on the percolation threshold in quantum entanglement percolation of small world networks. We show that Quantum Entanglement Percolation (QEP) has a better percolation performance than Classical Entanglement Percolation (CEP). By using different q swaps in Watts Strogatz (WS) small world networks and Kleinberg networks for simulation, we also show that the percolation threshold is minimized when SCP is equal to the average degree of the network. Furthermore, we introduce quantum walk as a new scheme to have an extra reduction in the percolation threshold.

A super-dense coding protocol based on the n-GHZ state is proposed to enable the two communicating parties to choose the number of transmitted code words according to their demand and to adapt the quantum super-dense coding protocol to multiple transmitted code word scenarios. A method is proposed that combines entanglement purification and Quantum Neural Network (QNN) to improve the channel capacity of super-dense coding. By simulating a realistic quantum communication noise environment in the Cirq platform, the effect of purification and QNN on the enhancement of fidelity and channel capacity in super-dense coding communication scenarios with different dimensions under unitary and non-unitary noise conditions is analyzed. The experimental results show that the channel capacity of super-dense coding is improved in different degrees when purification and QNN are applied separately, and the combination of purification and QNN has a superimposed effect on the channel capacity enhancement of super-dense coding, and the enhancement effect is more significant in different dimensions.

Based on the three-level quantum system, when it is in resonance, according to any two lattice points closest to Hamiltonian coupling, electrons transition from high energy level to low energy level and release photons; Or absorb photons and transition from low energy level to high energy level, thus obtaining the physical process of quantum walking along a straight line under the condition of equal probability. Then, the optical radiation in the quantum walk is mapped into a Gaussian pulse of the electric field, and the Maxwell's equation is solved by the three dimensional finite-difference time-domain method to obtain the spatial electric distributio. Finally, the physical process ofquantum walking on two parallel lines is further discussed, involving some physical properties such as electromagnetic coupling or coherence, quantum state exchange and so on. The electric field coupling between two lines can be calculated by FDTD, which provides a useful tool for the design and analysis of quantum devices.

Understanding the predictions made by machine learning (ML) models and their potential biases remains a challenging and labor-intensive task that depends on the application, the dataset, and the specific model. We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into data and ML models by identifying biases and explaining predictions. It is deeply integrated into Amazon SageMaker, a fully managed service that enables data scientists and developers to build, train, and deploy ML models at any scale. Clarify supports bias detection and feature importance computation across the ML lifecycle, during data preparation, model evaluation, and post-deployment monitoring. We outline the desiderata derived from customer input, the modular architecture, and the methodology for bias and explanation computations. Further, we describe the technical challenges encountered and the tradeoffs we had to make. For illustration, we discuss two customer use cases. We present our deployment results including qualitative customer feedback and a quantitative evaluation. Finally, we summarize lessons learned, and discuss best practices for the successful adoption of fairness and explanation tools in practice.