Optimal Channel

Optimal channel research explores how to design and utilize channels—whether quantum or classical—to maximize information transmission and representation, particularly within the context of machine learning and quantum information processing. Current investigations focus on characterizing channel contraction under privacy constraints (in quantum channels) and optimizing channel architectures for time series forecasting (in classical channels), including exploring alternatives to traditional channel-dependent and channel-independent strategies. These studies are significant because they improve the efficiency and performance of information processing tasks, impacting areas such as quantum computing, privacy-preserving data analysis, and machine learning model design.

Papers

June 26, 2024

Contraction of Private Quantum Channels and Private Quantum Hypothesis Testing
Theshani Nuradha, Mark M. Wilde
Quantum Channel Contraction Theory Private Hypothesis Optimal Channel

October 18, 2023

Is Channel Independent strategy optimal for Time Series Forecasting?
Yuan Peiwen, Zhu Changsheng
Time Series Forecasting Long Term Time Series Forecasting Self Supervised Clustering Optimal Channel

December 4, 2022

Statistical Physics of Deep Neural Networks: Initialization toward Optimal Channels
Kangyu Weng, Aohua Cheng, Ziyang Zhang, Pei Sun, Yang Tian
Deep Neural Network Information Bottleneck New Initialization Information Propagation Mutual Information Maximization Statistical Mechanic Optimal Channel

Optimal Channel

Papers

Contraction of Private Quantum Channels and Private Quantum Hypothesis Testing

Is Channel Independent strategy optimal for Time Series Forecasting?

Statistical Physics of Deep Neural Networks: Initialization toward Optimal Channels