DARPA Researchers Highlight Application Areas for Quantum Computing

Amid efforts to explore quantum computers’ transformative potential, one foundational element remains missing from the discussion about quantum: What are the benchmarks that predict whether tomorrow’s quantum computers will be truly revolutionary? In 2021, DARPA’s Quantum Benchmarking program kicked off with the goal of reinventing the metrics critical to measuring quantum computing progress and applying scientific rigor to often unsubstantiated claims about quantum computing’s future promise.

Six months into the second phase of the program, five teams have highlighted research findings focused on specific applications where quantum computing might make outsized impact over digital supercomputers. Equally important, researchers estimated what size quantum computer is needed to achieve the desired performance and how valuable the computation would be. Pre-prints of these results are available on arxiv.org.

“Quantum Benchmarking set out to quantitatively measure progress towards specific, transformational computational challenges, as well as estimate the hardware-specific resources required for a given level of benchmark performance,” said Dr. Joe Altepeter, Quantum Benchmarking program manager in DARPA’s Microsystems Technology Office. “The findings in these pre-prints mark an important first step toward quantifying the impact of quantum computers. We’re intentionally publishing preliminary results, because we want robust feedback from the scientific and industrial communities. We want that feedback so by the end of the program we have results we — and the community — can really trust.”

In the first phase of the program, eight interdisciplinary teams compiled more than 200 potential applications from which they created 20 candidate benchmarks that could quantify progress in using quantum computers to solve hard computational tasks with economic utility. For the second phase, DARPA selected specific benchmarks for detailed study in three broad categories: chemistry, materials science, and non-linear differential equations. DARPA then selected five teams for the second phase to refine these chosen benchmarks according to rigorous, utility-driven criteria, and then expand those benchmarks’ applications, incorporate scalable and robust testing, evaluate real-world utility, and create tools for estimating resources and performance needed to run end-to-end instantiations of the applications on realistic quantum hardware. The pre-prints describe the teams’ results in these areas, showing that it is plausible that quantum computers will provide advantage for economically valuable applications in certain chemistry, quantum materials, and materials science applications. It is unclear, at this point, whether any advantage can be achieved for applications in nonlinear differential equations.

Three teams — University of Southern California, HRL Laboratories, and L3Harris — focused on benchmarks and applications while two other teams — Rigetti Computing and Zapata Computing — estimated required quantum computing resources. MIT Lincoln Laboratory, NASA, and Los Alamos National Laboratory provided subject matter expertise, software integration, and test and evaluation capabilities.

Throughout the remainder of the second phase, teams will continue to optimize the quantum algorithms studied for the various applications, improve the utility estimates to understand the value proposition of future quantum computers, and continue to develop the software tools.

The findings address potential quantum computing applications, not the hardware needs. A companion effort to the Quantum Benchmarking initiative — the US2QC program — is addressing whether a quantum computer for these applications can actually be built.

To view the pre-print titles, abstracts, and links to the full text, as well as open-source software code developed by the teams, visit: Publications highlighting potential impact of quantum computing in specific applications.

The papers, which have not yet been peer-reviewed, are publicly available on arXiv preprint server (https://arxiv.org/). It is anticipated that following community input, teams will submit final versions of the papers for publication in peer-reviewed journals.