Quantum Computing Holds Promise for Economic Benefit Says Zapata AI

Researchers have made significant progress in harnessing the power of quantum computing to solve complex problems in condensed matter physics, with potential economic benefits estimated at $22.1 billion. The Fermi-Hubbard model, used to describe electron behavior in lattices, could be efficiently solved using quantum computers, leading to breakthroughs in superconductors, magnets for MRI machines, and energy-dense batteries.

Classical computers struggle to solve this problem, with the Department of Energy spending $117 million annually on operating costs for high-performance computing facilities. A fast and accurate Fermi-Hubbard solver could save an estimated $8 million, 0.5 GWh, and 1.9 metric tons of CO2. The research, conducted in collaboration with L3Harris, North Carolina State University, Rigetti, Lockheed Martin, and the MIT Lincoln Laboratory, highlights the need for further quantum algorithms and hardware advances to realize this potential value.

Computational Fluid Dynamics (CFD)

The research team’s new quantum approach for estimating drag force is an exciting development, but it’s still in its infancy. The resources required to run this algorithm on a fault-tolerant quantum computer are currently impractical. However, as they matured, we’ve seen significant improvements in resource requirements in other areas like quantum chemistry. With further research, the same could be true for CFD.

The paper identifies avenues for reducing these resource costs, and more research is needed to realize the potential value at stake for this quantum use case. While it’s early days for quantum computing in CFD, the potential benefits are worth exploring further.

Condensed Matter Physics (Fermi-Hubbard Model)

Now, let’s dive into the Fermi-Hubbard model, a crucial area of condensed matter physics. If quantum computing could enable an efficient and exact solver for this model, it could generate an estimated $22.1 billion in present-day value.

The potential benefits are staggering:

• A fast and accurate Fermi-Hubbard solver could save an estimated $8 million, 0.5 GWh, and 1.9 metric tons of CO2 annually.
• It could lead to more efficient and powerful MRI devices, energy-dense batteries, and perhaps most significantly, a room-temperature superconductor.
• A room-temperature superconductor would be worth an average of $22.1 billion in present value, with far-reaching implications for energy transmission, fusion power, maglev trains, and more.

The research provides a detailed resource breakdown for the task of estimating experimentally relevant outputs for the Fermi-Hubbard model, highlighting the need for improvement along multiple axes to make these promising use cases a reality with quantum computers.

Looking Ahead

These utility estimates reaffirm quantum computing’s transformative potential. The upside potential is clear with billions of dollars at stake across just these three use cases. As the technology evolves, we’ll uncover new opportunities to optimize processes, reduce costs, and improve results across various industries.

While considerable research and innovation are still needed in quantum hardware and algorithms, the resource estimates provided will serve as a valuable baseline for improving methodologies. The future of quantum computing looks bright, and collaborations between researchers and industry partners will be crucial in bringing these use cases closer to reality.