Using Artificial Intelligence and Quantum Computing to Enhance U.S. Department of Homeland Security Mission Capabilities

Nicolas M. Robles, Elie Alhajjar, Jesse Geneson, Alvin Moon, Christopher Scott Adams, Kristin J. Leuschner & Joshua Steier

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T he U.S. Department of Homeland Security (DHS) is the third-largest cabinet department in the federal government, bringing together multiple components, including the Federal Emergency Management Agency (FEMA), the Countering Weapons of Mass Destruction (WMD) Office, the U.S. Coast Guard (USCG), and the U.S. Secret Service (USSS), among others. These components are charged with carrying out a diverse array of missions: protecting the United States against terrorism, securing U.S. borders, securing cyberspace and critical infrastructure, preserving U.S. economic security, and strengthening disaster preparedness and resilience.1 To successfully achieve these missions, DHS must leverage technologies to the fullest extent possible.

DHS employs well-tested technologies to manage the complexity and resource the costs of its missions. However, two powerful emerging technologies—artificial intelligence (AI) and quantum computing (QC)—might have the potential to significantly expand the capabilities available to DHS in the future. AI—in particular, its subfield of machine learning (ML)—is an umbrella concept of using computers to rapidly solve problems for which the development of algorithms by human programmers would be costprohibitive or otherwise extremely difficult (Murphy, 2012).

QC attempts to leverage the principles of quantum mechanics (QM) to obtain quantifiable advantages over traditional computing, both in terms of speed and in the ability to solve very complex problems. Unlike previous leaps in the progress or advancement of science, such as the nuclear program or the space program, which were state sponsored, QC is, for the most part, incentivized and pioneered by private and for-profit companies and by academic institutions (Parker, 2021; Parker et al., 2022). AI is more mature than QC as a domain, and research in AI is distributed widely through academia and industry.