Quantum Computing for Computational Chemistry SBIR/STTR (QC3 SBIR/STTR)

to Section III.C.3 Third Component: Budget Justification Workbook / SF-424A. DE-FOA-0003483 - Quantum Computing for Computational Chemistry SBIR/STTR (QC3 SBIR/STTR) To obtain a copy of the Notice of Funding Opportunity (NOFO) please go to ARPA-E eXCHANGE at https://arpa-e-foa.energy.gov. To apply to this NOFO, Applicants must register with and submit application materials through ARPA-E eXCHANGE (https://arpa-e-foa.energy.gov/Registration.aspx). For detailed guidance on using ARPA-E eXCHANGE, please refer to the ARPA-E eXCHANGE User Guide (https://arpa-e-foa.energy.gov/Manuals.aspx). ARPA-E will not review or consider application materials submitted through other means. For problems with ARPA-E eXCHANGE, email ExchangeHelp@hq.doe.gov (with NOFO name and number in the subject line). Questions about this NOFO? Check the Frequently Asked Questions available at http://arpa-e.energy.gov/faq. For questions that have not already been answered, email ARPA-E-CO@hq.doe.gov. Agency Overview: The Advanced Research Projects Agency – Energy (ARPA-E), an organization within the Department of Energy (DOE), is chartered by Congress in the America COMPETES Act of 2007 (P.L. 110-69), as amended by the America COMPETES Reauthorization Act of 2010 (P.L. 111-358), as further amended by the Energy Act of 2020 (P.L. 116-260): “(A) to enhance the economic and energy security of the United States through the development of energy technologies that— (i) reduce imports of energy from foreign sources; (ii) reduce energy-related emissions, including greenhouse gases; (iii) improve the energy efficiency of all economic sectors; (iv) provide transformative solutions to improve the management, clean-up, and disposal of radioactive waste and spent nuclear fuel; and (v) improve the resilience, reliability, and security of infrastructure to produce, deliver, and store energy; and (B) to ensure that the United States maintains a technological lead in developing and deploying advanced energy technologies.” ARPA-E issues this Notice of Funding Opportunity (NOFO) under its authorizing statute codified at 42 U.S.C. § 16538. The NOFO and any cooperative agreements or grants made under this NOFO are subject to 2 C.F.R. Part 200 as supplemented by 2 C.F.R. Part 910. ARPA-E funds research on, and the development of, transformative science and technology solutions to address the energy and environmental missions of the Department. The agency focuses on technologies that can be meaningfully advanced with a modest investment over a defined period of time in order to catalyze the translation from scientific discovery to early-stage technology. For the latest news and information about ARPA-E, its programs and the research projects currently supported, see: http://arpa-e.energy.gov/. ARPA-E funds transformational research. Existing energy technologies generally progress on established “learning curves” where refinements to a technology and the economies of scale that accrue as manufacturing and distribution develop drive improvements to the cost/performance metric in a gradual fashion. This continual improvement of a technology is important to its increased commercial deployment and is appropriately the focus of the private sector or the applied technology offices within DOE. In contrast, ARPA-E supports transformative research that has the potential to create fundamentally new learning curves. ARPA-E technology projects typically start with cost/performance estimates well above the level of an incumbent technology. Given the high risk inherent in these projects, many will fail to progress, but some may succeed in generating a new learning curve with a projected cost/performance metric that is significantly better than that of the incumbent technology. ARPA-E will provide support at the highest funding level only for submissions with significant technology risk, aggressive timetables, and careful management and mitigation of the associated risks. ARPA-E funds technology with the potential to be disruptive in the marketplace. The mere creation of a new learning curve does not ensure market penetration. Rather, the ultimate value of a technology is determined by the marketplace, and impactful technologies ultimately become disruptive – that is, they are widely adopted and displace existing technologies from the marketplace or create entirely new markets. ARPA-E understands that definitive proof of market disruption takes time, particularly for energy technologies. Therefore, ARPA-E funds the development of technologies that, if technically successful, have clear disruptive potential, e.g., by demonstrating capability for manufacturing at competitive cost and deployment at scale. ARPA-E funds applied research and development (R&D). The Office of Management and Budget defines “applied research” as an “original investigation undertaken in order to acquire new knowledge…directed primarily towards a specific practical aim or objective” and defines “experimental development” as “creative and systematic work, drawing on knowledge gained from research and practical experience, which is directed at producing new products or processes or improving existing products or processes.” Applicants interested in receiving financial assistance for basic research (defined by the Office of Management and Budget as “experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts”) should contact the DOE’s Office of Science (http://science.energy.gov/). Office of Science national scientific user facilities (http://science.energy.gov/user-facilities/) are open to all researchers, including ARPA-E Applicants and awardees. These facilities provide advanced tools of modern science including accelerators, colliders, supercomputers, light sources and neutron sources, as well as facilities for studying the nanoworld, the environment, and the atmosphere. Projects focused on early-stage R&D for the improvement of technology along defined roadmaps may be more appropriate for support through the DOE applied energy offices including: the Office of Energy Efficiency and Renewable Energy (http://www.eere.energy.gov/), the Office of Fossil Energy and Carbon Management (https://www.energy.gov/fecm/office-fossil-energy-and-carbon-management), the Office of Nuclear Energy (http://www.energy.gov/ne/office-nuclear-energy), and the Office of Electricity (https://www.energy.gov/oe/office-electricity). ARPA-E encourages submissions stemming from ideas that still require proof-of-concept R&D efforts as well as those for which some proof-of-concept demonstration already exists. Submissions can propose a project with the end deliverable being an extremely creative, but partial solution. Program Overview: The Quantum Computing for Computational Chemistry program (QC3) aims to harness the transformative power of quantum computing to accelerate energy innovation. Computation plays an essential role in modern R&D, but classical computers struggle to simulate quantum systems with the speed, scale, and accuracy necessary to advance many commercial energy applications. This program will support the R&D of scalable, generalizable quantum computing approaches to computational chemistry and materials science. These approaches will be exponentially faster than the classical computing state-of-the-art (SoA), improving speed, accuracy, or problem size by 100 times (100x). This could result in a cumulative energy impact of 1 quadrillion British thermal units (1 quad), which is equal to a reduction of roughly 1 gigaton of carbon dioxide equivalent (CO2e) emissions from energy-related activities. The development of electronic digital computers sparked revolutionary advances in science. Not only did computers accelerate hand calculations, but they also created entirely new modes of computation from the Monte Carlo methods developed at Los Alamos National Lab, to computational chemistry simulations, to artificial intelligence. Despite the power of classical (i.e., nonquantum) computational methods, many important problems—especially in chemistry and materials science—remain frustratingly out of reach. The first quantum revolution resulted in the fundamental laws that determine the behavior of atoms and molecules, but exact solutions of these laws are staggeringly difficult to calculate. As R. B. Laughlin and David Pines wrote, “[the Schrödinger Equation] cannot be solved accurately when the number of particles exceeds about 10. No [classical] computer existing, or that will ever exist, can break this barrier because it is a catastrophe of dimension.” The computational cost of exact solutions grows exponentially with the number of particles. It is precisely this difficulty that led Richard Feynman to first propose the idea of a quantum computer: If nature is fundamentally quantum, our simulations of nature should be quantum as well. The QC3 program focuses on developing and applying quantum algorithms in key energy research areas where classical methods are insufficient. This includes the development of quantum chemistry algorithms, their translation into quantum circuits or analog programs, and rigorous validation against classical benchmarks and experiments. The goal is to validate these algorithms on a quantum computer with approximately 100 logical qubits to show scalability and practical advantages over classical computation for energy applications. Limitations of Classical Computational Chemistry Simulations Classical simulations generally fall into two categories: exact or scalable. Exact methods capture the complete quantum physics of nature. These methods are accurate, but their computational complexity grows exponentially with system size, giving rise to the so-called "catastrophe of dimension." Several families of scalable methods, such as quantum Monte Carlo and Density Functional Theory (DFT) have enabled scientists to make significant strides in understanding chemical behavior and material characteristics. The scalability comes at the expense of uncontrolled approximations or limitations on types of systems that can be studied. DFT, for instance, is a powerful method that can simulate hundreds or even thousands of atoms, but the exact functional encoding electron-electron correlations is unknown. There are many materials for which DFT is simply not able to produce accurate results. Scalable classical methods are especially limited with regard to simulations of dynamics and simulations of materials with strongly correlated electrons. Classical methods remain powerful tools, but they are fundamentally limited, regardless of future advances in classical computing hardware. These limitations affect a wide range of problems with important energy applications central to ARPA-E’s mission, including heterogeneous catalysis, photoexcitation, and high-temperature superconductivity. Potential of Quantum Computing Quantum computing presents a promising solution to these challenges by operating within an inherently quantum mechanical framework. Quantum algorithms offer the potential for massive improvements in performance over classical methods, providing both greater accuracy and scalability. In contrast to the “catastrophe of dimension” suffered by classical computers, the cost of simulating a quantum system on a quantum computer is often polynomial in problem size. A combination of developments in hardware and error correction algorithms has recently led to the first logical qubits, a critical step on the path to achieving fault-tolerant quantum computing. In the past year, multiple quantum hardware vendors have announced roadmaps to quantum computers with more than 100 logical qubits by 2030. These devices have the potential to unlock the first transformative energy applications of quantum computers, but the potential may not be realized without focused development of quantum algorithms for energy applications. Optimizing the Full Quantum Computational Stack A key part of QC3 will be optimizing the entire computational stack, which encompasses all software and hardware layers involved in quantum computing (see Figure 2 in Section I.D). Optimizing through the stack includes: 1. Applications: Identifying a problem important in the energy sector; 2. Algorithms/Software: Refining quantum algorithms and workflows, co-optimized with quantum error correction; and 3. Hardware: Optimizing for specific hardware. Finally, all recipients must run their solution on real quantum hardware. The QC3 program strives to push forward ARPA-E’s mission by advancing, optimizing, and co-designing each of these interconnected levels in the context of some of the most urgent challenges in energy. Illustrative Application Areas The following use cases represent illustrative examples of key opportunity spaces where quantum computing can significantly advance ARPA-E’s mission. While these areas are highlighted as examples, applicants are also encouraged to propose other innovative applications of quantum computing that align with the program's overarching goals. Catalysts: Catalysts are central to various industrial processes, including chemical synthesis, fuel production, and emissions reduction. By improving the understanding of reaction mechanisms and networks, new catalysts can be designed that will require less energy and produce fewer emissions.6, , In addition, there is a great opportunity to discover and design new catalytically active surfaces while reducing the experimental validation parameter space. X-ray absorption spectroscopy (XAS) for materials diagnostics: XAS can reveal details of atomic structure deep inside materials. This can lead to a better understanding of phenomena like lithiation states in battery degradation and oxidation states in fuels for advanced nuclear reactors. , , However, classical computers struggle to generate accurate spectra for comparison to experimental data. Quantum algorithms for the analysis of XAS would enable rapid progress in the development of new energy materials.13 Superconductors: Understanding and optimizing the properties of superconductors through quantum simulation could lead to the discovery of materials with higher critical superconductor temperatures.7 Advances in this area could revolutionize transmission, making the power grid more efficient and significantly reducing overall energy consumption. Battery Chemistry: Quantum computing can model the complex chemical interactions in battery materials more accurately, leading to the design of batteries with longer lifespans and higher energy densities. Quantum simulations could also accelerate the development of next-generation batteries, such as solid-state batteries, which promise to outperform current lithium-ion technology and contribute to the decarbonization of the transportation and energy sectors. Earth-abundant magnetic materials: Strong magnets made of rare-earth elements are essential for efficiently transforming between electrical and mechanical energy in applications ranging from wind turbines to electric motors. However, these materials are heavy, expensive, and vulnerable to supply chain risks. Quantum computers could help design new permanent magnets made of earth-abundant materials. To view the NOFO in its entirety, please visit https://arpa-e-foa.energy.gov