Challenge
Discovering new quantum algorithms is an exceptionally difficult challenge due to the number of potential quantum operations and is highly counterintuitive for human researchers to navigate. U.S. leadership in the emerging quantum computing revolution will require accelerating the design and development of quantum algorithms (including those that capitalize on the convergence of classical HPC, AI, and quantum computers) that demonstrate scientific utility and a provable quantum advantage.
AI Solution
Novel AI could discover new quantum algorithms by automating and optimizing their design and translating them into applications without requiring prior domain knowledge. Furthermore, AI-powered platforms can translate high-level problem descriptions in natural language into executable quantum circuits, making algorithm design more accessible to researchers from various fields. AI could help establish scientific workflows that leverage the interplay of classical and quantum resources, managing data flow and executing complex computations across platforms.
Justification
There is strong evidence that quantum computers and algorithms will offer solutions to computational problems with high impact to the scientific community, beyond the limits of classical HPC and AI. DOE hosts the most complete suite of scientific computing capabilities and these advances in quantum capability will enable computations that are classically intractable.
National Impact
The discovery of new quantum algorithms will have broad applications to science domains, such as fusion sciences, high energy physics, nuclear physics, materials science, and chemistry, with proposed commercial applications for the acceleration of drug, material, and chemical discovery. This technological leap would not only bolster the nation’s economy and security but also provide tools to address some of the most challenging scientific and societal problems.
Aligned Actions from the National Laboratories
Speeding Up Essential Groundwork for Quantum Computing
National Challenge: Discovering Quantum Algorithms with AI
Leading Lab: Pacific Northwest National Laboratory
Quantum computers have the potential to dramatically accelerate complex calculations, but inefficient data preparation remains a major bottleneck to achieving quantum advantage. To meet this national challenge, Pacific Northwest National Laboratory developed “Picasso,” an AI-enabled parallel algorithm that uses classical computing to efficiently prepare data for quantum systems. By leveraging AI-augmented graph coloring to optimize how quantum workloads are grouped, Picasso can process problems of unprecedented scale—handling millions of Pauli strings in minutes and one trillion-plus relationships while using modest GPU memory. This advance significantly expands the class of problems that can be explored with quantum algorithms, accelerating progress toward practical quantum computing.