Carnegie Mellon University

Fall Research Rundown

A collection of cutting-edge research happening at the Tepper School.

Applications of GenAI for Entrepreneurs

Sean Ammirati, Distinguished Service Professor of Entrepreneurship

Generative artificial intelligence (GenAI) is a type of artificial intelligence that learns the structure and patterns of generative models’ input to generate text, images, or other media. In most situations, GenAI serves as a tool to provide guidance to entrepreneurs rather than taking full control and conducting work independently. In a new article, a researcher shows that GenAI supports entrepreneurs primarily by offering new perspectives and solutions to innovation challenges; it also automates routine cognitive tasks, freeing up time for entrepreneurs to focus on critical strategic decisions. This combination ensures that an entrepreneur’s intellectual efforts are allocated efficiently, significantly improving both the pace and quality of a venture’s development. GenAI, the author suggests, has the potential to redefine entrepreneurship and how startups are built. He addresses questions that might arise around use of this new technology, including the illusion of correctness in GenAI, information boundaries, long-term memory, and intellectual property issues.

The article, Applications of GenAI for Entrepreneurs, appears as a working paper and is authored by Ammirati, S (Carnegie Mellon University). Copyright 2023. All rights reserved.

Optimal Entanglement Distillation Policies for Quantum Switches

Sridhar R. Tayur, Ford Distinguished Research Chair; University Professor of Operations Management

The quantum switch is a vital part for the development of the quantum internet, a global network of quantum computers, sensors, and other devices that will someday send, compute, and receive information encoded in quantum states. The main challenge in realizing entanglement-distribution-based quantum networks as part of the quantum internet is in the reliability of creating quantum entanglement across large distances at high rates. In a new study, researchers from Tepper and the University of Pittsburgh considered a discrete time model for a quantum switch that attempted to generate fresh elementary entanglement with clients in each time step. Their model helps capture the role of entanglement distillation in mitigating the effects of decoherence in a quantum switch in an entanglement distribution network. In so doing, their work adds to the growing literature on quantum switches and provides a foundation for further investigation into a wide range of scenarios.

The mathematical model underlying the analysis is Markov Decision Process theory, a well-known method in Operations Management research. Bringing this expertise to quantum communication research is part of the larger program at the Quantum Technology Group (QTG), founded in 2018 by Sridhar Tayur, professor of operations management at the Tepper School. The goal of the QTG is to explore and advance business applications that can benefit from the second quantum revolution including quantum computing and quantum information science. For more information, visit the QTG website

The article, Optimal Entanglement Distillation Policies for Quantum Switchesappears in Quantum Networking & Communications Conference and is authored by Kumar, V (University of Pittsburgh), Chandra, NK (University of Pittsburgh), Seshadreesan, KP (University of Pittsburgh), Scheller-Wolf, A (Carnegie Mellon University), and Tayur, S (Carnegie Mellon University). Copyright 2023. All rights reserved.