The U.S. Department of Energy (DOE) has announced 12 Phase II project awards totaling $159 million under the federal Genesis Mission, capping an initial cohort of 297 projects across the multi-agency scientific initiative. Administered through the DOE Office of Science, the awards pair high-performance computing (HPC) nodes and artificial intelligence models (“Super Intelligence”) with DOE national laboratory testbeds to address national science and technology challenges. Out of the 12 selected Phase II projects, two specifically target physical quantum hardware scaling, quantum error correction (QEC) co-design, and functional quantum materials synthesis.
Led by Harvard University, the Application-Aware Error Correcting Codesign for Scientific Quantum Computing (ASQC) project combines advanced AI optimization with physical quantum processors to automate QEC code selection and system co-design. Parallelly, Oak Ridge National Laboratory (ORNL) leads the AI-Empowered Design of Functional Quantum Magnets initiative, establishing a physics-informed AI framework that works backward from target physical properties to design new quantum magnetic materials for quantum sensing networks, low-power microelectronics, and QPU hardware platforms.
| [ DOE Genesis Mission Phase II Quantum & Microelectronics Awards ] | ||
|---|---|---|
| Project & Lead Entity | Technical Domain & Architecture Focus | Strategic Target & System Deliverable |
| • ASQC Project (Harvard University) | • Application-aware QEC co-design • Algorithmic circuit optimization | • Integrates AI decoders with quantum hardware to reduce error-correction overheads for scientific workloads. |
| • Quantum Magnets (Oak Ridge National Lab / BNL) | • Physics-informed material synthesis • Quantum sensing & low-power state control | • Inverse design of functional quantum materials for sensors, cryogenic electronics, and QPUs. |
| • AXESS Project (Fermilab) | • Extreme-environment microelectronics • Cryogenic & radiation-hardened ICs | • AI-driven “specs-to-silicon” workflow to compress custom chip design cycles from months to minutes for ultra-cold QPU environments. |
| • MOAT-Core Project (Lawrence Berkeley National Lab / BNL) | • AI accelerator assistant (“Osprey”) • Autonomous particle accelerator tuning & digital twins | • Deploys multi-lab platform across 16 institutions to optimize accelerator beamlines for EUV lithography, QPU microelectronics, and material characterization. |
A key focus across the awards is custom microelectronics and quantum characterization led by Fermi National Accelerator Laboratory (Fermilab). Under the Phase II Accelerating eXtreme Environment Specs-to-Silicon (AXESS) project, Fermilab partners with AMD, IBM, SLAC, and academic institutions to apply AI models that compress chip design cycles from months to minutes. The resulting custom integrated circuits are optimized for extreme radiation and sub-Kelvin cryogenic operating regimes required by fault-tolerant quantum computing testbeds. Additionally, Fermilab received a Phase I award for the AI-Guided Sparse Characterization of Quantum Sensing States and Entanglement Structures (QCVV) project. Partnering with NVIDIA, IBM, Purdue, Quantum Machines, and the University of Chicago, QCVV deploys closed-loop AI agents to select optimal quantum state measurements, drastically reducing data acquisition overheads at the Superconducting Quantum Materials and Systems (SQMS) Center.
The hardware projects are supported by multi-facility software infrastructure scaled by Lawrence Berkeley National Laboratory under the Multi-Office Accelerator Team Core (MOAT-Core) project. MOAT-Core expands LBNL’s agentic AI assistant, Osprey, across 16 collaborating institutions and eight national laboratories, including Brookhaven National Laboratory. By integrating digital twins and real-time physics-constrained learning to automate particle accelerator operations, the platform optimizes high-energy beamlines and extreme ultraviolet (EUV) lithography tools used to engineer next-generation microelectronics, superconducting materials, and quantum computing components.
The Phase II awards operationalize broader national objectives outlined in the White House Office of Science and Technology Policy (OSTP) Genesis Mission framework. These include dedicated focus areas for AI-driven quantum algorithm discovery—automating circuit compilation without domain-specific knowledge—and system-level quantum control. Under the “Realizing Quantum Systems for Discovery and Use” challenge, DOE’s five National Quantum Information Research Centers and the Department of War’s Quantum Science office are deploying real-time AI agents for noise channel mitigation, adaptive QEC decoding, and multi-node quantum sensing network control.
Review the official funding release on Energy.gov here, examine national technology goals in the Genesis Mission Challenge Document here, inspect the complete award distribution on the DOE Office of Science Genesis Mission Selections List here, inspect Fermilab’s AXESS and QCVV workstreams in the Fermilab Newsroom here, examine BNL’s Phase II project roles in the Brookhaven National Laboratory Newsroom here, read LBNL’s Osprey platform details via the Berkeley Lab News Center here, and review our previous coverage on the DOE Quantum Genesis Launch here, DOE High Energy Physics Quantum Awards here, and PsiQuantum’s $150M OSC conditional loan commitment here.
October 8, 2026