PNNL Researchers Develop Algorithm for Quantum Computing with 85% Reduction in Computational Load

Pacific Northwest National Laboratory (PNNL) has developed a Picasso algorithm to prepare quantum data for large systems efficiently. By employing graph coloring and clique partitioning techniques, the algorithm reduces computational demands by 85%, enabling handling of nearly 50 times more Pauli strings than current methods. Tested on complex hydrogen models, this breakthrough supports advancements in quantum computing applications and was funded by PNNL and DOE’s Office of Science.

The algorithm, recently published on GitHub after being presented at the IEEE International Symposium on Parallel and Distributed Processing, tackles a critical scaling challenge in quantum computing: efficiently preparing classical data for quantum systems.

“Quantum computing can be extremely fast and efficient, but you have to address potential bottlenecks. Right now, preparing information for a quantum system is one factor holding us back,” explains Mahantesh Halappanavar, a leadership team member at the Center for AI @PNNL.

The innovation uses “graph coloring” and “clique partitioning” techniques that dramatically reduce computational demands. When testing hydrogen model systems generating over 2 million quantum elements (Pauli strings), Picasso processed the trillion-plus relationships in just 15 minutes—a problem size nearly 50 times larger than current tools can handle.

S M Ferdous, the paper’s first author and a Linus Pauling Distinguished Postdoctoral Fellow, explains their breakthrough: “From the perspective of high-performance computing, this type of problem really presents itself as a clique partitioning problem. We can represent an extremely large amount of data using graphic analytics and reduce the computation necessary.”

The team’s approach leverages “sparsification”—using only about one-tenth of the total dataset to perform accurate calculations. This memory-efficient technique allows quantum preparation to scale to previously unmanageable problem sizes.

The researchers believe Picasso can be extended to address even larger quantum systems requiring 100 to 1,000 qubits—the leading edge of quantum computing development. As a bonus, they’ve developed an AI algorithm helping users calculate the optimal tradeoff between data volume and memory requirements.

For quantum computing to deliver on its revolutionary promise, these backstage optimizations may prove just as crucial as advances in quantum hardware itself.