US DOE Launches 2028 Quantum Supercomputing Push With $2 Billion Backdrop
Updated
Updated · New Scientist · Jul 8
US DOE Launches 2028 Quantum Supercomputing Push With $2 Billion Backdrop
3 articles · Updated · New Scientist · Jul 8
Summary
The Energy Department opened a competition to build a national quantum supercomputing facility, aiming to make quantum machines scientifically useful by 2028.
DOE says the systems should start contributing to chemistry, materials science, plasma physics and high-energy physics, speeding work on new materials, drugs and agricultural and manufacturing molecules.
Darío Gil said recent gains in qubit quality, error-correction algorithms and AI-assisted control make the target ambitious but achievable without a massive breakthrough.
The plan still faces steep scaling and supply-chain hurdles, with useful machines needing to grow hundreds or thousands of times beyond today’s error-prone devices.
The initiative follows Trump quantum executive orders and a $2 billion Commerce investment, while the UK and China pursue longer quantum timelines beyond 2030.
In the global race for quantum supremacy, what are the hidden risks of America's aggressive 2028 deadline?
With quantum error correction advancing so fast, is the biggest hurdle to quantum advantage now software, not hardware?
As public funds create private quantum giants, who will truly own the resulting scientific breakthroughs?
Racing to 2028: The U.S. Quantum Genesis Initiative and the Global Quantum Computing Race
Overview
In July 2026, the United States launched the Quantum Genesis Initiative to create a unified ecosystem combining high-performance computing, artificial intelligence, and quantum computing. This initiative aims to position the U.S. as a global leader by building one of the most powerful discovery platforms ever conceived. Central to this effort is the National Quantum Supercomputing User Facility, which will give scientists and engineers unprecedented access to advanced quantum systems. These systems are designed to solve complex challenges that current computational methods cannot address, opening new frontiers for scientific discovery and innovation across multiple disciplines.