The U.S. Department of Energy has selected IBM for a Phase I Genesis Mission project focused on AI-assisted quantum application development. The company also plans to provide up to $50 million in access to IBM quantum systems for DOE national laboratories and their partners over the next five years. The Genesis Mission is building a national scientific-computing framework that brings together AI, classical high-performance computing, quantum computing, scientific instruments, and research data. IBM’s selection adds a quantum component to the first set of projects under the Genesis Mission Request for Applications process. This work complements the DOE’s planned computing infrastructure at Oak Ridge National Laboratory. As previously reported, AMD’s Lux system is expected to become the first fully operational Genesis Mission platform, with funded projects slated to begin using it in October 2026. Lux combines AMD Instinct MI355X GPUs, EPYC CPUs, and Pensando networking to support AI services, conventional HPC workloads, simulation, and data-intensive research workflows. IBM’s contribution is directed at extending that model to include quantum resources for workloads where classical systems and AI alone are insufficient. AI-Assisted Quantum Application Development IBM’s Phase I project will examine a reversed workflow for identifying quantum computing applications. Rather than beginning with a scientific problem and searching for a quantum algorithm, IBM plans to start with known quantum algorithms and use an agentic AI research assistant to locate scientific problems in published literature that meet the algorithms’ requirements. The system would review research papers, identify possible algorithm-to-problem matches, assess them against human-defined criteria, and produce an explanation for researchers to evaluate. The objective is to reduce the manual effort involved in surveying scientific literature and uncover application candidates that may otherwise be missed. The DOE’s Phase I awards are intended to establish and test research workflows before larger-scale funding and deployment. Teams