Collaborative Research: NSF-ANR QISE: QUANTINT: Quantum Information and Network Theory: Algorithms and Performance Limits

Quantum information technologies are progressing at an ever-increasing pace. The potential for the quantum information and computing paradigm to solve complex problems exponentially faster than classical computers promises revolutionary advancements in data analysis and optimization, medicine, material science, and security and communications. This has led the government, the technology industry and the academia to invest in research towards realization of robust operations on quantum networks capable of processing quantum information in a distributed way. The QUANTINT project envisions an interconnected network of quantum devices exchanging qubits and utilizing unique quantum properties such as entanglement to enhance information processing algorithms. The project aims to design efficient universal algorithms and strategies to (i) compress distributed qubits and (ii) harness distributed entanglement in emerging tasks such as distributed learning. Achieving the above vision will amplify the performance of distributed learning and other information processing tasks by several orders of magnitude, with a great reduction of the burden on network capacity. The objective of QUANTINT is to characterize and reach the fundamental limits of quantum information and learning theory and to achieve performance breakthroughs in distributed information processing. The quantum toolbox from which the project draws includes phenomena such as superposition, entanglement and non-locality. Motivated by this, QUANTINT undertakes a comprehensive exploration of two centrally important tasks - compression and inference - in the context of quantum information. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.