The estimation of multiple parameters in quantum metrology is important for a vast array of
applications in quantum information processing. However, the unattainability of fundamental …

Functional brain networks are shaped and constrained by the underlying structural network.
However, functional networks are not merely a one-to-one reflection of the structural …

Graph-structured data arise in a variety of real-world context ranging from sensor and
transportation to biological and social networks. As a ubiquitous tool to process graph …

We must protect inherently fragile quantum data to unlock the potential of quantum
technologies. A pertinent concern in schemes for quantum storage is their potential for near …

This paper presents a methodology for synthesizing discrete MIMO PID controllers through
iterative solutions of LMIs. It justifies the necessity of direct synthesis of discrete controllers in …

Spectral clustering techniques are valuable tools in signal processing and machine learning
for partitioning complex data sets. The effectiveness of spectral clustering stems from …

Spectral graph filters are a key component in state-of-the-art machine learning models used
for graph-based learning, such as graph neural networks. For certain tasks stability of the …

This paper investigates the secrecy achievable region of multiple access wiretap (MAC-WT)
channels where, besides confidential messages, the users have also open messages to …

Parameterless stop** criteria for recursive polynomial expansions to construct the density
matrix in electronic structure calculations are proposed. Based on convergence-order …

In this paper, we present the development of a quantum computing method for calculating
the value at risk (V a R) for a portfolio of assets managed by a finance institution. We extend …