The U.S. Department of Energy has announced a new competition aimed at accelerating the development of a fault-tolerant, scientifically relevant quantum computer by 2028. The initiative, called the Quantum Genesis Q Competition, will allocate up to $215 million in funding, contingent on congressional approval, to support private-sector efforts in creating the first machine capable of performing complex scientific calculations reliably.

The program offers up to $1.5 million per awardee in the initial phase, with $250,000 released upfront to cover prototype development. The remaining funds will be disbursed only after participants validate their prototypes within approximately one year. Up to ten teams are expected to receive this phase-one support.

Success in the first phase unlocks access to a $100 million incentive pool for the next stage, focused on scaling the technology to meet the department’s benchmark: a system with at least 100 logical qubits capable of executing 100,000 error-corrected operations. Logical qubits, which are constructed from multiple physical qubits to mitigate errors, represent a critical milestone in building practical quantum computers. Current systems, including those with thousands of physical qubits like Caltech’s 6,100-qubit array, have not yet achieved fault tolerance at the required scale.

QuEra, a quantum computing company, has already demonstrated a prototype architecture with up to 96 logical qubits and plans to launch its Libra system by 2028 with over 256 logical qubits and a target logical error rate of 10⁻⁶. While this places it near the DoE’s performance goals, distinctions between operational metrics and error rates mean direct comparisons remain nuanced. The company has not formally confirmed participation in the competition.

The effort underscores a broader push to transition quantum computing from experimental platforms to tools capable of addressing real-world scientific challenges. Whether the 2028 deadline will be met remains uncertain, as the technical hurdles of error correction and system scalability continue to pose significant challenges.