DARPA’s Quantum Benchmarking Program: A Key Step Toward the Future of Quantum Computing

Launched in 2021, the Quantum Benchmarking Program aims to provide a comprehensive and systematic approach to tracking the advancements of quantum computing. This initiative focuses on developing critical tools and metrics to gauge the progress of quantum systems as they move closer to solving real-world, computationally difficult problems. By creating standardized quantum computing metrics, the program helps assess how quantum devices perform relative to classical systems, providing a clear picture of their capabilities and limitations. These metrics are essential for identifying where quantum computers can offer practical solutions and where they still face significant challenges.

In addition to these performance metrics, the program also seeks to estimate the resources required—both quantum and classical—to reach specific milestones in quantum computing. By providing a clearer understanding of the necessary computational power and infrastructure, DARPA hopes to offer guidance on the feasibility of solving complex problems using quantum systems. This resource estimation is vital for setting realistic expectations and ensuring that the development of quantum technologies is aligned with practical applications in industries such as healthcare, energy, and materials science.

Program Phases

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.

The first phase of the program already made significant strides, with eight interdisciplinary teams identifying over 200 potential quantum computing applications and proposing 20 candidate benchmarks. In Phase 2, DARPA has selected specific benchmarks for deeper analysis, organized into three broad categories: chemistry, materials science, and non-linear differential equations. These areas represent some of the most promising and computationally intensive fields, where the potential of quantum computing could lead to groundbreaking discoveries and innovations. Through this targeted approach, the program is helping to pave the way for quantum computing to transition from experimental systems to practical, real-world tools.

Phase 2 Awards

“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,” said DARPA.

Rigetti Computing, a prominent player in quantum-classical computing, recently advanced to Phase 2 of the DARPA Benchmarking Program. This phase is focused on refining the resource estimation framework developed during Phase 1 and applying it to some of the most pressing utility-scale challenges. Key areas of study include dynamic chemistry simulations and modeling quantum system dynamics—tasks that are currently beyond the capabilities of classical computing. By focusing on these complex problems, the program aims to provide actionable insights that could accelerate the development of quantum technologies for practical use.

Homogeneous Catalyst Discovery:

The industrial manufacturing of chemicals consumes a significant amount of energy and raw materials. In principle, the development of new catalysts could greatly improve the efficiency of chemical production. However, the discovery of viable catalysts can be exceedingly challenging because it is difficult to know the efficacy of a candidate without experimentally synthesizing and characterizing it.

Zapata AI investigated the potential for fault-tolerant quantum computers to expedite the discovery of catalysts for nitrogen fixation, a crucial industrial process. Their research suggests that quantum computing could offer a distinct advantage over classical methods in this area, enabling faster identification and optimization of catalysts. This could lead to significant time and cost savings in the chemical manufacturing industry, particularly in sectors that rely on efficient nitrogen fixation processes, such as fertilizers and energy production.

Computational Fluid Dynamics (CFD):

Across industries, traditional design and engineering workflows are being upgraded to simulation driven processes. Many workflows include computational fluid dynamics (CFD). Simulations of turbulent flow are notorious for high compute costs and reliance on approximate methods that compromise accuracy. Improvements in the speed and accuracy of CFD calculations would potentially reduce design workflow costs by reducing computational costs and eliminating the need for experimental testing.

L3 Harris examined the possibilities of utilizing quantum computers to enhance computational fluid dynamics (CFD) simulations, especially in industries like ship design. While quantum computing holds promise for improving CFD simulations, their initial findings indicate that considerable advancements in quantum algorithms are required before these technologies can be applied effectively to incompressible fluid dynamics. Despite the challenges, their research points to the potential long-term benefits of quantum computing in fluid dynamics optimization, particularly for complex engineering designs.

NMR Spectral Prediction:

Nuclear magnetic resonance (NMR) spectroscopy is a widely used tool in chemistry, medicine, and solid-state physics. Traditional NMR spectrometers require strong magnetic fields to analyze samples, but recent advances in atomic magnetometry allow NMR to be performed in much weaker magnetic fields, even below the strength of the Earth’s magnetic field. This zero-to-ultralow field (ZULF) regime is beneficial because it reduces relaxation effects and reveals spin interactions that are usually hidden, all with smaller, more efficient equipment. However, interpreting the resulting spectra can be challenging due to complex spin interactions, requiring computational methods to analyze the data.

MIT Lincoln Laboratory explored the application of quantum computing to simulate nuclear magnetic resonance (NMR) spectra, a valuable technique in chemistry, medicine, and physics. Their findings suggest that fault-tolerant quantum computation could significantly enhance the accuracy and efficiency of NMR spectral predictions. This advancement could lead to improved insights in various scientific fields, potentially revolutionizing areas such as drug development, molecular analysis, and material characterization, where precise NMR data is essential.

Strongly Correlated Systems:

Understanding the physics of strongly correlated materials is a major challenge in modern physics. Many fascinating materials, such as high-temperature superconductors and spin liquids, exhibit medium to strong correlations. Developing a comprehensive understanding of these materials is crucial, but it’s difficult due to the competing effects of kinetic energy and Coulomb repulsion. This competition makes it hard to accurately describe these interacting materials using traditional analytical and numerical methods.

North Carolina State University conducted research on using quantum computers to explore strongly correlated materials, a long-standing challenge in condensed matter physics. Their results indicate that while quantum computing has the potential to offer new insights into these complex systems, substantial progress in both quantum hardware and algorithm development is still necessary. The study highlights the promising future of quantum computing in tackling difficult problems related to material properties and behaviors, although the technology is not yet ready to fully address these challenges.

Open System Simulation:

Researchers at the University of Southern California explored how quantum simulators could be leveraged to study materials undergoing metal-insulator transitions, a key topic in material science. Their findings suggest that quantum simulation could provide a cost-effective approach to exploring these complex materials, potentially accelerating the discovery and development of new materials with unique properties. The ability to model such transitions accurately using quantum computers could revolutionize the materials discovery process, leading to advancements in technologies such as semiconductors and superconductors.

We explore the applications of a fully controllable open system quantum simulator, which could be realized by a universal quantum computer, though certain classical methods can also approximate it. We present two specific computational problems, where solving them would have significant scientific and industrial value. One example is the computation of the nonequilibrium behavior of Ca3Co2O6, a material previously studied through expensive MagLab experiments. By using an affordable quantum computer simulation to screen materials before sending them to MagLab, costs could be reduced by approximately $2M per material, offering a substantial financial benefit.