Jerry Chow | Searcharxiv

arXiv2025

Enabling Technologies for Scalable Superconducting Quantum Computing

Experiments with superconducting quantum processors have successfully demonstrated the basic functions needed for quantum computation and evidence of utility, albeit without a sizable array of error-corrected qubits. The realization of the full potential of quantum computing centers on achieving large scale fault-tolerant quantum computers. Science, engineering and industry advances are needed to robustly generate, sustain, and efficiently manipulate an exponentially large computational (Hilbert) space as well as supply the number and quality components needed for such a scaled system. In this article, we suggest critical areas of quantum system and ecosystem development, with respect to the handling and transmission of quantum information within and out of a cryogenic environment, that would accelerate the development of quantum computers based on superconducting circuits.

quant-ph↗

arXiv2012

Power Control and Interference Management in Dense Wireless Networks

We address the problem of interference management and power control in terms of maximization of a general utility function. For the utility functions under consideration, we propose a power control algorithm based on a fixed-point iteration; further, we prove local convergence of the algorithm in the neighborhood of the optimal power vector. Our algorithm has several benefits over the previously studied works in the literature: first, the algorithm can be applied to problems other than network utility maximization (NUM), e.g., power control in a relay network; second, for a network with $N$ wireless transmitters, the computational complexity of the proposed algorithm is $\mathcal{O}(N^2)$ calculations per iteration (significantly smaller than the $\mathcal{O}(N^3) $ calculations for Newton's iterations or gradient descent approaches). Furthermore, the algorithm converges very fast (usually in less than 15 iterations), and in particular, if initialized close to the optimal solution, the convergence speed is much faster. This suggests the potential of tracking variations in slowly fading channels. Finally, when implemented in a distributed fashion, the algorithm attains the optimal power vector with a signaling/computational complexity of only $\mathcal{O}(N)$ at each node.

cs.IT↗